Project Title: Predicting the Entrance Exam Outcome to IBA (Undergraduate Program)

Wali Ullah (ERP-09745)

Background of the study

Given the importance of higher education at individual, economic and societal level, it is imperative to investigate the factors that influence academic performance. The income disparity across countries and within a country necessitates inquiry into impact of socioeconomic differences (posh areas vs lower-class areas, peaceful areas vs areas with social unrest etc.) on academic performance. The gender of students is an important factor in determining academic performance. There is a general perception among masses that females are better in qualitative skills whereas males are better in quantitative skills however literature has been unable to establish the superiority of either gender with most research efforts revealing mixed results. Another significant factor that impacts academic performance in higher education institutes is prior qualification whether candidate is coming from private institutes or public, international curriculum or national, arts background or science etc.
This research project attempts to determine the relationship between the identified intellectual and non- intellectual factors and the extent of their impact on academic performance. Therefore, this research project aims to answer the following research questions:

  1. Does socioeconomic background have a role in academic performance of students?

  2. Does academic curriculum have any impact on quantitative and qualitative skills of students?

  3. Does gender of students affect the academic performance?

  4. Does female show better performance in qualitative skills whereas males in quantitative skills?

  5. Are female candidates more interested in BBA type programs whereas male in quantitatively rigorous programs such as BSCS, BSEM and BSACF?

In continuation of the fact that males tend to have a tilt towards quantitative side and females are dominant with qualitative or soft skills. The degree program of BBA & SSLA is dominated by female applicants. Whereas, the degrees such as BSACF & BSCS, namely the “good” degree as they branch out from science and technology, tend to be swept away by men.

Socioeconomic Background

The study uses location as a proxy for socioeconomic status of applicants. Since, most of the applicant pool for IBA is from Karachi we will divide Karachi into three groups based on the income status of the residents in those areas. For instance, all affluent areas of Karachi will be clustered in Karachi-1, middle income areas will be clustered in Karachi-2, and all the rest will be clustered in Karachi-3. Other cities are not being classified similarly.

• Karachi-1 includes DHA, Clifton, PECHS, Bahadurabad,

• Karachi-2- includes area Gulshan Iqbal, North Nazimabad, Saddar

• Karachi-3- includes all other areas of Karachi

Academic Curriculum

regarding the academic curriculum, we consider four different types of Boards (curriculum) from which the students come to IBA. At the SSC (qualification level 1) level these are: • O-Level, Matriculation, Agha Khan Board, and other international Boards And the HSC (Qualification level 2) level, these are: • A-Level, Intermediate, Aga Khan Board and Other international boards

To compute the performance score, each applicant’s score in mathematics and verbal section is divided by the total attainable marks of each section to obtain attained score. Research Question 1: Does socioeconomic background have a role in academic performance of students? (ANOVA-of score with city)

Socioeconomic status (SES) is believed to have a strong correlation with measures of academic performance; however, weak, and moderate correlations are frequently reported in the literature. This project uses location as a proxy for socioeconomic status of applicants. Since, most of the applicant pool for IBA is from Karachi we divided Karachi into three groups based on the income status of the residents in those areas. For instance, all affluent areas of Karachi are clustered in Karachi-1, middle income areas are clustered in Karachi-1, and all the rest are clustered in Karachi-3.

Research Question 2: Does academic curriculum have any impact on quantitative and qualitative skills of students? (ANOVA-of across board)

Students with O/A Level background are more equipped with quantitative and qualitative skills hence tend to perform better academically. Nevertheless, in some areas vocational qualification comes at par to A Level. Initially it was known that sound performance in mathematics has a direct connection to the attainment of a degree/graduation from college. However, for the science-based degree, prior knowledge in mathematics and desirable graduation result did not have a direct relationship.

Research Question 3: Does gender of students affect the academic performance? (ANOVA-gender)

Performance of both genders tend to vary specifically based on the following four categories: verbal skills, visual-spatial ability, mathematical ability, and aggression. Research clearly indicates that men outperformed women in subjects such as economics. Yet, at some instances, gender alone is a weak predictor of academic performance.

Research Question 4: Does female show better performance in qualitative skills whereas males in quantitative skills? (English math comparison-across gender graph) To compute the performance score, each applicant’s score in mathematics and verbal section is divided by the total attainable marks of each section to obtain attained score.

Females tend to be star performers in the components of qualitative elements. The left hemisphere dominance of the brain allows their verbal ability / speaking potential or qualitative element to be strong. On the contrary, male applicants are better off with hard skills / technical subjects that involve mathematics.

Research Question 5: Are female candidates more interested in BBA type programs whereas male in quantitatively rigorous programs such as BSCS, BSEM and BSACF? (Percentage comparison of male and females in BBA, ACF with CS and EM)

In continuation of the fact that males tend to have a tilt towards quantitative side and females are dominant with qualitative or soft skills. The degree program of BBA & SSLA is dominated by female applicants. Whereas, the degrees such as BSACF & BSCS, namely the “good” degree as they branch out from science and technology, tend to be swept away by men.

In [1]:
import TemplateML as template
from sklearn.model_selection import train_test_split
from sklearn.tree import export_graphviz
import pydot
from IPython.display import Image
In [2]:
FILE_NAME = "FinalData_testing.csv"
LABEL_COL = "test_successful"
df = template.load_data(FILE_NAME)
display(df.head())
print(df.shape)
print(df.dtypes)
term_name gender date_of_birth place_of_birth postal_address city1 Province city countryname seat_no ... cert_degree1 discipline_Mat medium cert_degree2 studied_maths discipline2 Eng_Score Math_Score Year program
0 Fall 2014 Male NaN Gilgit Near CSD shop, Defense Colony, Jutial, Gilgit Gilgit Gilgit Others Pakistan 801 ... Aga Khan Board Science English Aga Khan Board Yes Science 70.63 26.0 2014 BSCS
1 Fall 2014 Male NaN Mardan Katlang road sultan muhmmad kali shanker mardan Mardan KPK Others Pakistan 806 ... Matriculation Science English Intermediate Yes Science 45.63 52.5 2014 BSEM
2 Fall 2014 Male NaN Danyore,Gilgit Tehsil Danyore,District Gilgit,Gilgit Baltistan GilGit Gilgit Others Pakistan 807 ... Aga Khan Board Science English Aga Khan Board Yes Science 80.00 58.0 2014 BSEM
3 Fall 2014 Male NaN Gahkuch Paeen Village and P/o Gahkuch Paeen Tehsil Punial, Gahkuch Gilgit Others Pakistan 808 ... Aga Khan Board Science English Aga Khan Board Yes Science 69.38 51.0 2014 BSCS
4 Fall 2014 Male NaN D.I.KHAN VILLAGE DURRIKHAIL P/O ATHOG TEHSIL PAHARPUR D... D.I.KHAN KPK Others Pakistan 810 ... Matriculation Science English Intermediate Yes Science 60.00 23.5 2014 BSEM

5 rows × 23 columns

(38931, 23)
term_name                object
gender                   object
date_of_birth            object
place_of_birth           object
postal_address           object
city1                    object
Province                 object
city                     object
countryname              object
seat_no                  object
test_center              object
test_successful          object
interview_successful    float64
cert_degree1             object
discipline_Mat           object
medium                   object
cert_degree2             object
studied_maths            object
discipline2              object
Eng_Score               float64
Math_Score              float64
Year                      int64
program                  object
dtype: object
In [3]:
df.columns
Out[3]:
Index(['term_name', 'gender', 'date_of_birth', 'place_of_birth',
       'postal_address', 'city1', 'Province', 'city', 'countryname', 'seat_no',
       'test_center', 'test_successful', 'interview_successful',
       'cert_degree1', 'discipline_Mat', 'medium', 'cert_degree2',
       'studied_maths', 'discipline2', 'Eng_Score', 'Math_Score', 'Year',
       'program'],
      dtype='object')

Data Set:

For any given research, the dataset plays the backbone role as it provides a collection of information based on specified characteristics. In this case, the data consisted of:

  1. Entry Test Scores of English (Verbal) Component
  2. Entry Test Scores of Mathematics (Quantitative) Component
  3. Success Ratio in the Entry Test (Yes/NO)
  4. Number of Years over which the Test was conducted (2014 to 2020)
  5. Specific attributes of the applicants, such as: a. Gender b. Age c. Prior Academic Background d. Area of Residence As a first off run at the cluster of information, the data set helped retrieve 29 attributes (columns/attributes) across 38,931 rows (observations).
In [4]:
display(df.head())
print(df.shape)
print(df.dtypes)
term_name gender date_of_birth place_of_birth postal_address city1 Province city countryname seat_no ... cert_degree1 discipline_Mat medium cert_degree2 studied_maths discipline2 Eng_Score Math_Score Year program
0 Fall 2014 Male NaN Gilgit Near CSD shop, Defense Colony, Jutial, Gilgit Gilgit Gilgit Others Pakistan 801 ... Aga Khan Board Science English Aga Khan Board Yes Science 70.63 26.0 2014 BSCS
1 Fall 2014 Male NaN Mardan Katlang road sultan muhmmad kali shanker mardan Mardan KPK Others Pakistan 806 ... Matriculation Science English Intermediate Yes Science 45.63 52.5 2014 BSEM
2 Fall 2014 Male NaN Danyore,Gilgit Tehsil Danyore,District Gilgit,Gilgit Baltistan GilGit Gilgit Others Pakistan 807 ... Aga Khan Board Science English Aga Khan Board Yes Science 80.00 58.0 2014 BSEM
3 Fall 2014 Male NaN Gahkuch Paeen Village and P/o Gahkuch Paeen Tehsil Punial, Gahkuch Gilgit Others Pakistan 808 ... Aga Khan Board Science English Aga Khan Board Yes Science 69.38 51.0 2014 BSCS
4 Fall 2014 Male NaN D.I.KHAN VILLAGE DURRIKHAIL P/O ATHOG TEHSIL PAHARPUR D... D.I.KHAN KPK Others Pakistan 810 ... Matriculation Science English Intermediate Yes Science 60.00 23.5 2014 BSEM

5 rows × 23 columns

(38931, 23)
term_name                object
gender                   object
date_of_birth            object
place_of_birth           object
postal_address           object
city1                    object
Province                 object
city                     object
countryname              object
seat_no                  object
test_center              object
test_successful          object
interview_successful    float64
cert_degree1             object
discipline_Mat           object
medium                   object
cert_degree2             object
studied_maths            object
discipline2              object
Eng_Score               float64
Math_Score              float64
Year                      int64
program                  object
dtype: object
In [5]:
df.isnull().sum()
Out[5]:
term_name                   0
gender                      0
date_of_birth            3450
place_of_birth              1
postal_address            549
city1                       0
Province                    0
city                        0
countryname                 0
seat_no                   385
test_center             11937
test_successful             0
interview_successful     1637
cert_degree1                0
discipline_Mat              0
medium                      0
cert_degree2                0
studied_maths               0
discipline2                 0
Eng_Score                   0
Math_Score                  0
Year                        0
program                     0
dtype: int64

Data Wrangling

Effective data management in today’s time has become such an important element of our profession that it allows us to oversee the execution happening according to the research’s true essence. Similarly, for the purpose of this investigation, candidate’s address was used to root down to their area, city and province details in SQL. In order to streamline the findings, rows with missing English and Mathematics scores were removed. Consequently, the final data that was closely considered and evaluated consisted of:

  1. 23 Columns (Consisting of the candidate’s details/specified attributes)
  2. 38,931 Rows (highlighting the number of observations made)

The dataset was composed such that it has two specific numeric columns populated by the scores in English and Mathematics. Moreover, one data column was specially dedicated the year in which the test was conducted along with the remaining 20 strings under scrutiny.

In [6]:
df = template.cleaningup(df, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['term_name','date_of_birth', 'place_of_birth', 
                                         'postal_address', 'city1', 'countryname','seat_no',
                                         'medium','test_center','interview_successful', 
                                         'discipline_Mat'], to_date=['Year'])
df is all cleaned up..
In [7]:
df.columns
Out[7]:
Index(['gender', 'Province', 'city', 'test_successful', 'cert_degree1',
       'cert_degree2', 'studied_maths', 'discipline2', 'Eng_Score',
       'Math_Score', 'Year', 'program'],
      dtype='object')
In [8]:
template.correlation_anlysis(df)
In [9]:
template.basicanalysis(df)
template.stringcolanalysis(df)
template.numcolanalysis(df)
Shape is:
 (38931, 12)

 Columns are:
 Index(['gender', 'Province', 'city', 'test_successful', 'cert_degree1',
       'cert_degree2', 'studied_maths', 'discipline2', 'Eng_Score',
       'Math_Score', 'Year', 'program'],
      dtype='object')

 Types are:
 gender                     object
Province                   object
city                       object
test_successful            object
cert_degree1               object
cert_degree2               object
studied_maths              object
discipline2                object
Eng_Score                 float64
Math_Score                float64
Year               datetime64[ns]
program                    object
dtype: object

 Statistical Analysis of Numerical Columns:
           Eng_Score    Math_Score
count  38931.000000  38931.000000
mean      44.496195     35.989989
std       17.656205     17.088383
min        0.560000      0.500000
25%       32.500000     23.500000
50%       44.440000     36.000000
75%       56.670000     48.000000
max       97.780000     97.500000

The Descriptive Analysis of Data

The final data has a total of 38,931 rows (observations) and 12 columns (attributes). To further bifurcate the results the findings and analysis is mentioned herewith:

  1. The descriptive analysis of two numeric columns show that average English score is 44.49 with Standard Deviation of 17.65. Maximum and Minimum English score is 97.79% and 0.5% respectively. The average Mathematics score is about 36 with Standard Deviation of 17, maximum score of 97.5% and minimum of 0.5%. Moreover, it can be concluded that the average English score is higher than average Mathematics score, while both have similar volatility.
  2. The English and Mathematics scores boxplot show that there are some outliers. However, the two numeric columns English score and Mathematics score seems to be Normally distributed.
  3. The counts and percentage analysis of categorical variables show that:

a. Gender: 64% of the subject are male and 36% are females

b. Province: 88% of the applicants are from Sindh, while 12% from other provinces (which includes Punjab, AJK, KPK, Baluchistan and Gilgit etc)

c. City: Since Sindh populated the most number of applicants, it was evident because 50% candidates are from Karachi-3, 15% and 12% from Karachi 1 and 2 respectively. Hyderabad has 4%, Lahore 2% and Islamabad 2%, while other cities have less than 1%.

d. Test Success Ratio: Only 16% had the success ratio, whereas the remaining 84% of the candidates were unsuccessful as they failed the test.

e. Higher Education: 50% of applicants are coming from A/O level background, 3% from Aga Khan Board, about 47% from traditional Intermediate and secondary Boards and about 1% from other International Boards.

f. Studied Mathematics/ Science Background: 63% of the applicant have studied Mathematics in Intermediate and A level and about 54% of applicants are coming from Science background.

g. Program Selection: The applicant for BBA are 50%, BSACF are 25%, BSCS are 12%, SSLA are 6%, BSEM are 5% and BSECO are about 1% in the entire sample.

Normality Test of The Two Numeric Columns

In [10]:
template.Normality_test(df)
Normality Test for all numric columns
['Eng_Score', 'Math_Score']
Normaility Test for Column: Eng_Score
Statistics=0.997, p_value=0.000
Sample does not look Gaussian (reject H0)
Normaility Test for Column: Math_Score
Statistics=0.991, p_value=0.000
Sample does not look Gaussian (reject H0)

The test-statistics for Normality show that both English and Mathematics scores are not Normally Distributed.

Mean Equality T-Test and Chi-Square Test of Independence

In [11]:
template.t_test(df)
template.chisquare_test(df)
t-test for equality of mean between all numric columns
['Eng_Score', 'Math_Score']
(Eng_Score,Math_Score) => t-value=68.30516117668779, p-value=0.0
Chisquare-test for Independence between all numric columns
(Eng_Score,Math_Score) => chisqr-value=262956.6947531656, p-value=0.0
Dependent (reject H0)

The t-test suggests that mean score of English and Mathematics are not equal. However, the two scores are dependent of each other, subsequently, indicating that even though the English and Mathematics scores are not equal yet a candidate that is performing well in the English section also manages to score well in the quantitative section.

Percentage Counts Analysis of Categorical Columns

Research Question 5: Does female candidate are interested in BBA, SSLA and BSECO and type programs where as male in adcamically rigorous programs such as BSCS, BSEM and BSACF?

This hypothesis can be duly tested through the percentage count of male and female candidates in these two types of programs.

In [12]:
template.Count_Per(df, label_col=['gender'], cat_list=['program'])
==================***==================***==================
Analysis of Column gender
==================***==================***==================
=======================
Analsis of gender by program
=======================
                 %                                                     
program        BBA      BSACF       BSCS     BSECO      BSEM       SSLA
gender                                                                 
Female   53.276925  19.862326   8.697835  1.183135  4.424208  12.555571
Male     48.949370  28.156894  14.472684  1.224735  4.446668   2.749650
<Figure size 360x1080 with 0 Axes>
In [13]:
template.Count_Per1(df, label_col=['gender'], cat_list=['program'])
Analsis of gender by program

In lieu of the research question mentioned above, the bar plot as well as the table show that females candidates are more inclined toward the BBA and SSLA program, whereas BSCS, and BSACF are male dominant programs. The percentage count of male and female candidates is almost same in both Economics programs (BSECO and BSEM). This result validates our hypothesis female applicants are more inclined towards BBA and SSLA type program, while male applicants towards the academically rigger type programs such as BSCS, and BSACF.

All comparisons in terms of percentage counts are given in Appendix-F with relevant explanations.

Average English and Mathematics Scores Comparisons

Research Question 4: Does female show better performance in English (qualitative skills) whereas males in Mathematics (quantitative skills)?

This hypothesis can be verified by comparing the average English and Mathematics scores of both male and female candidates for the two types of programs. (English math comparison-across gender graph)

In [14]:
template.Avg_by_cat(df,cat_list=['gender'])
=====================******=====================
Analysis of Average English and Mathematics Score by gender
=====================******=====================
        Eng_Score  Math_Score
gender                       
Female  46.492503   33.792770
Male    43.381906   37.216421
In [15]:
template.Avg_by_cat1(df,cat_list=['gender'])
Analysis of Average English and Mathematics Score by gender

The bar plot shows that average English score of female candidates are significantly higher than the male candidates, whereas of average Mathematics score of male is four times greater than the female candidates. Consequently, this verifies the Scientific research that women tend to display far superior potential when it comes to learning verbal abilities. They tend to rely more on this component as compared to any other strategy for easier recall. As opposed to gender differences in verbal ability favoring females, gender differences in mathematics performance favor males. Anastasi's (1958) text on differential psychology, states that differences in numerical aptitude favor boys.

Average English and Mathematics scores comparison across all string variables is given in Appendix-E.

ANOVA Analysis

ANOVA analysis is carried out to see whether the socio-economic status (denoted by the proxy “city”), curriculum (denoted by the proxy “Higher Education Board such as A-level, Intermediate etc.”) and gender has impact on English and Mathematics score of the candidates.

Research Question 1: Does socioeconomic background have a role in academic performance of students? (ANOVA of English and Mathematics scores with city)

This hypothesis can be empirically evaluated through ANOVA test of English and Mathematics scores using the string column city, which indicates the socio-economics classes in our sample.

In [16]:
template.ANOVA_analysis1(df,cat=['city'])
============+++++============+++++============
Analysis of Columns Eng_Score by city
============+++++============+++++============

Anova => - city
sum_sq df F PR(>F)
Intercept 2.877417e+06 1.0 9450.781736 0.000000e+00
C(Q("city")) 2.857754e+05 8.0 117.327487 6.429085e-195
Residual 1.185032e+07 38922.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by city
============+++++============+++++============

Anova => - city
sum_sq df F PR(>F)
Intercept 1.986099e+06 1.0 6808.878721 0.000000e+00
C(Q("city")) 1.480408e+04 8.0 6.344046 2.966714e-08
Residual 1.135325e+07 38922.0 NaN NaN

The F-Statistics tend to be highly significant across socio-economics classes (city) for both English and Mathematics scores, which suggest that the income level and socio-economic class is an important determinant of the academic performance.

Research Question 2: Does academic curriculum have any impact on quantitative and qualitative skills of students? (ANOVA of English and Mathematics scores across Boards)

For further analysis of this hypothesis, it was empirically evaluated through ANOVA test of English and Mathematics scores using the string column cert_degree (which has the entries of Higher Education Board such as: A/O level, Aga Khan Board, Matric/Intermediate and other International Boards), ultimately highlighting the different types of curriculums in our sample. For this there are two variables cert_degree1 (it is curriculum type at 10 year of Education) and cert_degree2 (it is curriculum type at 12 years of education)

In [17]:
template.ANOVA_analysis1(df,cat=['cert_degree1','cert_degree2'])
============+++++============+++++============
Analysis of Columns Eng_Score by cert_degree1
============+++++============+++++============

Anova => - cert_degree1
sum_sq df F PR(>F)
Intercept 2.058587e+06 1.0 7410.358798 0.0
C(Q("cert_degree1")) 1.322233e+06 3.0 1586.560912 0.0
Residual 1.081387e+07 38927.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by cert_degree1
============+++++============+++++============

Anova => - cert_degree1
sum_sq df F PR(>F)
Intercept 1.135856e+06 1.0 4553.895797 0.0
C(Q("cert_degree1")) 1.658690e+06 3.0 2216.683801 0.0
Residual 9.709370e+06 38927.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by cert_degree2
============+++++============+++++============

Anova => - cert_degree2
sum_sq df F PR(>F)
Intercept 4.639299e+07 1.0 160595.265526 0.0
C(Q("cert_degree2")) 8.908121e+05 3.0 1027.886759 0.0
Residual 1.124529e+07 38927.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by cert_degree2
============+++++============+++++============

Anova => - cert_degree2
sum_sq df F PR(>F)
Intercept 3.115302e+07 1.0 113883.053281 0.0
C(Q("cert_degree2")) 7.194733e+05 3.0 876.702924 0.0
Residual 1.064859e+07 38927.0 NaN NaN

The F-Statistics are statistically significant across curriculums (for both certificate degree 1 and certificate-degree_2) for both English and Mathematics scores. Hence, enunciating the fact that that the curriculum which is taught at 9-10th classes and 11-12th classes is an important determinant of the academic performance in the entry test.

Research Question 3: Does gender of students affect the academic performance? (ANOVA of English and Mathematics scores across gender)

The hypothesis was empirically evaluated through ANOVA test of English and Mathematics scores using the string column gender.

In [18]:
template.ANOVA_analysis1(df,cat=['gender'])
============+++++============+++++============
Analysis of Columns Eng_Score by gender
============+++++============+++++============

Anova => - gender
sum_sq df F PR(>F)
Intercept 3.014502e+07 1.0 97391.215552 0.000000e+00
C(Q("gender")) 8.660059e+04 1.0 279.785440 1.384166e-62
Residual 1.204950e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by gender
============+++++============+++++============

Anova => - gender
sum_sq df F PR(>F)
Intercept 1.592565e+07 1.0 55044.080352 0.000000e+00
C(Q("gender")) 1.049090e+05 1.0 362.598437 1.779221e-80
Residual 1.126315e+07 38929.0 NaN NaN

For which the F-statistics results proved to be highly statistically significant across gender (male and female) for both English and Mathematics scores, which suggest that applicant gender is an important determinant of the academic performance in the entry test.

Preparing Data for Machine Learning Algorithms

The one hot encoding is applied to string columns to create dummies, except the test_successful column which results in Rows=38931, and columns= 28). The data shows that 92% of applicants are from Sindh province. Therefore, the province variable is categorized in two group, Province_Sindh and Province_No_Sindh (all other provinces are groped in Province_No_Sindh).

In [19]:
df1=template.apply_label_encoding(df, cols=['test_successful'])
In [20]:
df1 = template.cleaningup(df1, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['Year', 'program'])
df is all cleaned up..
In [21]:
df1 = template.onehotencoding(df1)
In [22]:
df1.columns
Out[22]:
Index(['test_successful', 'Eng_Score', 'Math_Score', 'gender_Female',
       'gender_Male', 'Province_AJK', 'Province_Balochistan',
       'Province_Foreign', 'Province_Gilgit', 'Province_Islamabad',
       'Province_KPK', 'Province_Punjab', 'Province_Sindh', 'city_Hyderabad',
       'city_Islamabad', 'city_Karachi-1', 'city_Karachi-2', 'city_Karachi-3',
       'city_Lahore', 'city_Others', 'city_Peshawar', 'city_Quetta',
       'cert_degree1_Aga Khan Board', 'cert_degree1_Matriculation',
       'cert_degree1_O-Level', 'cert_degree1_Other boards',
       'cert_degree2_A-Level', 'cert_degree2_Aga Khan Board',
       'cert_degree2_Intermediate', 'cert_degree2_Other boards',
       'studied_maths_No', 'studied_maths_Yes', 'discipline2_Arts',
       'discipline2_Science'],
      dtype='object')
In [23]:
df1["Province_No_Sindh"]= df1['Province_AJK']+df1['Province_Balochistan']+df1['Province_Foreign']+df1['Province_Gilgit']+df1['Province_Islamabad']+df1['Province_KPK']+df1['Province_Punjab']
              
df1 = template.cleaningup(df1, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['Province_AJK', 'Province_Balochistan','Province_Foreign',
                                         'Province_Gilgit', 'Province_Islamabad','Province_KPK', 'Province_Punjab'])
df is all cleaned up..
In [24]:
df1 = template.onehotencoding(df1)
In [25]:
df1.shape
Out[25]:
(38931, 28)
In [26]:
template.correlation_anlysis(df1)

Machine Learning Algorithms

Given the data set, there are three variables that are to be predicted

  1. Test_Successful, (Yes/No)
  2. Math_Score (Continuous Variable)
  3. Eng_Score (Continuous Variable) Followed by two regression type analysis on the following two elements that are closely considered:
  4. English Score
  5. Mathematics Score

Lastly, there is one classification-based scenario is considered, which is based on test_successful (1 for successful and 0 otherwise)

In the first step we try to predict the binary outcome variable test successful using the classification based algorithms. The binary variable test_sucessful is imbalance. The following command shows that 84% are test un-successful (zeros) and only 16% are ones. Therefore, it seems necessary to address the class imbalance. The application Random under sampling results in 12404 rows and 26 columns/attributes. In this stage we consider the Random Under Sampling to address the imbalancing issue. The results of ML algorithms with addressing the class imbalance issue are given here. The results without addressing the class imbalance issue are given in Appendix-A.

In [27]:
import seaborn as sns
import matplotlib.pyplot as plt
sns.countplot(x='test_successful', data=df1, palette='hls')
plt.show()

count_no_sub = len(df1[df1['test_successful']==0])
count_sub = len(df1[df1['test_successful']==1])
pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)
print("percentage of test un-successful is", pct_of_no_sub*100)
pct_of_sub = count_sub/(count_no_sub+count_sub)
print("percentage of test successful is", pct_of_sub*100)
percentage of test un-successful is 84.0692507256428
percentage of test successful is 15.930749274357195

Classification based Prediction of Success with Addressing Class Imbalancing

In [28]:
S_all= template.cleaningup(df1, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['Eng_Score','Math_Score'])
df is all cleaned up..
In [29]:
S_all_bal1 = S_all.copy()
SAll_bal=template.Random_UnderSampling(S_all_bal1,'test_successful')
print('Orignal data shape:', S_all.shape)
print('Resampled data shape:', SAll_bal.shape)
Orignal data shape: (38931, 26)
Resampled data shape: (12404, 26)

To predict the test outcome and select the best candidate ML algorithm, we have applied twelve (12) different machine learning classification algorithms and considered four different scenarios, which are:

a. Without focusing the cross validation (CV) and features selection

b. Only cross validation (CV: Stratified K-Fold) is considered

c. Only feature selection (random forest based algorithm is used) criterion is considered

d. Both cross validation (CV: Stratified K-Fold) and features selection (random forest based algorithm is used) are considered.

For features selection, we run the random forest based algorithm to extract the top candidate features.

In [30]:
#1. Classification based Algrithms Results without CV and RFFS and addressing class imbalancing
results_without_cv_reg_rffs= template.run_algorithms_clf(SAll_bal,'test_successful')
============ LogReg ===========
Prediction Vector: 
 [1 0 1 ... 0 0 1]

 Accuracy: 
 63.7646110439339

 Precision of event Happening: 
 66.73306772908366

 Recall of event Happening: 
 54.25101214574899

 AUC: 
 0.6372261682728861

 F-Score:
 0.5984814649397053

 Confusion Matrix: 
 [[912 334]
 [565 670]]
============================== 

============ KNN ===========
Prediction Vector: 
 [1 1 1 ... 1 1 0]

 Accuracy: 
 61.104393389762194

 Precision of event Happening: 
 60.44891640866873

 Recall of event Happening: 
 63.238866396761125

 AUC: 
 0.6111381522085247

 F-Score:
 0.6181242580134546

 Confusion Matrix: 
 [[735 511]
 [454 781]]
============================== 

============ GadientBoosting ===========
Prediction Vector: 
 [1 0 1 ... 0 0 1]

 Accuracy: 
 64.81257557436517

 Precision of event Happening: 
 66.48451730418944

 Recall of event Happening: 
 59.10931174089069

 AUC: 
 0.6478740065375193

 F-Score:
 0.6258036862408917

 Confusion Matrix: 
 [[878 368]
 [505 730]]
============================== 

============ AdaBoost ===========
Prediction Vector: 
 [1 0 1 ... 0 0 1]

 Accuracy: 
 63.804917372027404

 Precision of event Happening: 
 66.76616915422886

 Recall of event Happening: 
 54.33198380566802

 AUC: 
 0.6376310265724813

 F-Score:
 0.599107142857143

 Confusion Matrix: 
 [[912 334]
 [564 671]]
============================== 

============ SVM ===========
Prediction Vector: 
 [1 1 1 ... 1 0 1]

 Accuracy: 
 65.25594518339379

 Precision of event Happening: 
 64.0542577241899

 Recall of event Happening: 
 68.82591093117408

 AUC: 
 0.6527170345916651

 F-Score:
 0.663544106167057

 Confusion Matrix: 
 [[769 477]
 [385 850]]
============================== 

============ DecisionTree ===========
Prediction Vector: 
 [1 1 1 ... 1 1 1]

 Accuracy: 
 64.36920596533656

 Precision of event Happening: 
 63.984063745019924

 Recall of event Happening: 
 65.02024291497975

 AUC: 
 0.6437207972394253

 F-Score:
 0.6449799196787148

 Confusion Matrix: 
 [[794 452]
 [432 803]]
============================== 

============ DeepNeuralNetwork ===========
Prediction Vector: 
 [1 1 1 ... 1 0 1]

 Accuracy: 
 65.21563885530028

 Precision of event Happening: 
 63.88059701492538

 Recall of event Happening: 
 69.31174089068826

 AUC: 
 0.6523371956251909

 F-Score:
 0.6648543689320388

 Confusion Matrix: 
 [[762 484]
 [379 856]]
============================== 

============ RandomForest ===========
Prediction Vector: 
 [1 1 1 ... 1 1 1]

 Accuracy: 
 64.40951229343007

 Precision of event Happening: 
 63.990461049284576

 Recall of event Happening: 
 65.18218623481782

 AUC: 
 0.6441292297294663

 F-Score:
 0.6458082631367831

 Confusion Matrix: 
 [[793 453]
 [430 805]]
============================== 

============ NaiveBayes ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 60.25796049979847

 Precision of event Happening: 
 61.226330027051404

 Recall of event Happening: 
 54.97975708502024

 AUC: 
 0.6023466184909119

 F-Score:
 0.5793515358361775

 Confusion Matrix: 
 [[816 430]
 [556 679]]
============================== 

============ MultiLayerPerceptron ===========
Prediction Vector: 
 [1 1 1 ... 1 0 1]

 Accuracy: 
 65.05441354292624

 Precision of event Happening: 
 63.96054628224582

 Recall of event Happening: 
 68.2591093117409

 AUC: 
 0.6506855947127975

 F-Score:
 0.6603995299647474

 Confusion Matrix: 
 [[771 475]
 [392 843]]
============================== 

============ LightGbm ===========
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 4967, number of negative: 4956
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000691 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 9923, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500554 -> initscore=0.002217
[LightGBM] [Info] Start training from score 0.002217
Prediction Vector: 
 [1. 0. 1. ... 0. 0. 1.]

 Accuracy: 
 64.65135026199114

 Precision of event Happening: 
 67.61811023622047

 Recall of event Happening: 
 55.62753036437247

 AUC: 
 0.64611517991175

 F-Score:
 0.6103953798311862

 Confusion Matrix: 
 [[917 329]
 [548 687]]
============================== 

============ XgBoost ===========
[11:18:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Prediction Vector: 
 [1 1 1 ... 1 1 1]

 Accuracy: 
 65.01410721483273

 Precision of event Happening: 
 63.62286562731997

 Recall of event Happening: 
 69.39271255060729

 AUC: 
 0.6503343492698903

 F-Score:
 0.6638264910921766

 Confusion Matrix: 
 [[756 490]
 [378 857]]
============================== 

============ LightGbm ===========
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 4967, number of negative: 4956
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 9923, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500554 -> initscore=0.002217
[LightGBM] [Info] Start training from score 0.002217
Prediction Vector: 
 [1. 0. 1. ... 0. 0. 1.]

 Accuracy: 
 64.65135026199114

 Precision of event Happening: 
 67.61811023622047

 Recall of event Happening: 
 55.62753036437247

 AUC: 
 0.64611517991175

 F-Score:
 0.6103953798311862

 Confusion Matrix: 
 [[917 329]
 [548 687]]
============================== 

In [31]:
# 2. Classification based Algrithms Results with CV only and addressing class imbalancing
results_cv = template.run_algorithms_cv_clf(SAll_bal,'test_successful')
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000796 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000584 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[11:21:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:21:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000863 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000617 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000619 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000721 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000711 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000643 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000725 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
============ LogReg ===========
{'accuracy': 64.37242988224897, 'precision': 67.07863263547455, 'recall': 52.91283569684691, 'auc_val': 0.6437173393589946, 'f_score': 0.5839778783503413}
============================== 

============ KNN ===========
{'accuracy': 59.54426061188947, 'precision': 59.104046963583166, 'recall': 60.137914913511, 'auc_val': 0.5954393278271259, 'f_score': 0.5931794848416684}
============================== 

============ GadientBoosting ===========
{'accuracy': 62.58303007460165, 'precision': 63.00988916550974, 'recall': 57.07248974079268, 'auc_val': 0.6258194379512753, 'f_score': 0.58899699682759}
============================== 

============ AdaBoost ===========
{'accuracy': 64.29982974188349, 'precision': 66.99204150951577, 'recall': 52.54186795491144, 'auc_val': 0.6429914030439978, 'f_score': 0.5806882448372633}
============================== 

============ SVM ===========
{'accuracy': 61.87339164565517, 'precision': 61.769389593200025, 'recall': 56.99181860682562, 'auc_val': 0.6187238325281804, 'f_score': 0.5825271064557936}
============================== 

============ DecisionTree ===========
{'accuracy': 61.09942554131684, 'precision': 60.60941935605579, 'recall': 56.3308399563659, 'auc_val': 0.6109864422627396, 'f_score': 0.5742343925007559}
============================== 

============ DeepNeuralNetwork ===========
{'accuracy': 61.6719282056614, 'precision': 61.417439975742, 'recall': 57.55649057191836, 'auc_val': 0.6167095215832943, 'f_score': 0.5859762143650451}
============================== 

============ RandomForest ===========
{'accuracy': 61.23646382989786, 'precision': 60.68578404660136, 'recall': 57.088826554464696, 'auc_val': 0.6123587086385123, 'f_score': 0.5791066046344249}
============================== 

============ NaiveBayes ===========
{'accuracy': 58.23730212367758, 'precision': 56.64873229963943, 'recall': 53.782089242117294, 'auc_val': 0.5823722144304192, 'f_score': 0.5415082974746674}
============================== 

============ MultiLayerPerceptron ===========
{'accuracy': 61.69608926204154, 'precision': 61.649833932727276, 'recall': 56.60472183263207, 'auc_val': 0.6169508077502467, 'f_score': 0.5796162442944286}
============================== 

============ LightGbm ===========
{'accuracy': 61.0106963686933, 'precision': 61.55426734178381, 'recall': 53.00875279206275, 'auc_val': 0.610099864942081, 'f_score': 0.5577695719362594}
============================== 

============ XgBoost ===========
{'accuracy': 61.74442437160459, 'precision': 61.453228834338084, 'recall': 56.86263051270064, 'auc_val': 0.6174366266687444, 'f_score': 0.5792053014885182}
============================== 

In [32]:
## 3. Classification based Algrithms Results with RFFS only and  addressing class imbalancing
res_rffs = template.MachineLearningwithRFFS_clf(SAll_bal,'test_successful', threshold=5,
                                    algo_list=template.get_supported_algorithms_clf())
cert_degree2_Intermediate      16.765584
studied_maths_No               13.986408
cert_degree2_A-Level           13.199462
studied_maths_Yes               9.010187
cert_degree1_Other boards       5.807934
cert_degree1_Matriculation      4.979888
cert_degree1_O-Level            4.431025
discipline2_Science             3.817486
discipline2_Arts                3.125780
city_Others                     2.932969
gender_Female                   2.634058
city_Karachi-2                  2.258575
gender_Male                     2.216016
city_Karachi-3                  2.064187
Province_Sindh                  1.776447
city_Karachi-1                  1.719548
Province_No_Sindh               1.577353
cert_degree1_Aga Khan Board     1.460297
city_Hyderabad                  1.424049
cert_degree2_Aga Khan Board     1.176838
city_Islamabad                  1.017953
city_Lahore                     0.806581
cert_degree2_Other boards       0.781467
city_Peshawar                   0.643353
city_Quetta                     0.386554
dtype: float64
Selected Features =['cert_degree2_Intermediate', 'studied_maths_No', 'cert_degree2_A-Level', 'studied_maths_Yes', 'cert_degree1_Other boards']
(12404, 26)
============ LogReg ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.918178153970175

 Precision of event Happening: 
 67.79661016949152

 Recall of event Happening: 
 48.582995951417004

 AUC: 
 0.6285490086495408

 F-Score:
 0.5660377358490566

 Confusion Matrix: 
 [[961 285]
 [635 600]]
============================== 

============ KNN ===========
Prediction Vector: 
 [1 1 1 ... 1 1 1]

 Accuracy: 
 63.72430471584038

 Precision of event Happening: 
 62.89453425712086

 Recall of event Happening: 
 66.15384615384615

 AUC: 
 0.6373502901592789

 F-Score:
 0.6448303078137333

 Confusion Matrix: 
 [[764 482]
 [418 817]]
============================== 

============ GadientBoosting ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.87787182587666

 Precision of event Happening: 
 67.88154897494306

 Recall of event Happening: 
 48.259109311740886

 AUC: 
 0.6281334277786081

 F-Score:
 0.5641268338854708

 Confusion Matrix: 
 [[964 282]
 [639 596]]
============================== 

============ AdaBoost ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.918178153970175

 Precision of event Happening: 
 67.79661016949152

 Recall of event Happening: 
 48.582995951417004

 AUC: 
 0.6285490086495408

 F-Score:
 0.5660377358490566

 Confusion Matrix: 
 [[961 285]
 [635 600]]
============================== 

============ SVM ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.87787182587666

 Precision of event Happening: 
 68.77990430622009

 Recall of event Happening: 
 46.558704453441294

 AUC: 
 0.628058369779245

 F-Score:
 0.5552873008208595

 Confusion Matrix: 
 [[985 261]
 [660 575]]
============================== 

============ DecisionTree ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.87787182587666

 Precision of event Happening: 
 68.77990430622009

 Recall of event Happening: 
 46.558704453441294

 AUC: 
 0.628058369779245

 F-Score:
 0.5552873008208595

 Confusion Matrix: 
 [[985 261]
 [660 575]]
============================== 

============ DeepNeuralNetwork ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.87787182587666

 Precision of event Happening: 
 67.88154897494306

 Recall of event Happening: 
 48.259109311740886

 AUC: 
 0.6281334277786081

 F-Score:
 0.5641268338854708

 Confusion Matrix: 
 [[964 282]
 [639 596]]
============================== 

============ RandomForest ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.87787182587666

 Precision of event Happening: 
 68.77990430622009

 Recall of event Happening: 
 46.558704453441294

 AUC: 
 0.628058369779245

 F-Score:
 0.5552873008208595

 Confusion Matrix: 
 [[985 261]
 [660 575]]
============================== 

============ NaiveBayes ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.918178153970175

 Precision of event Happening: 
 67.79661016949152

 Recall of event Happening: 
 48.582995951417004

 AUC: 
 0.6285490086495408

 F-Score:
 0.5660377358490566

 Confusion Matrix: 
 [[961 285]
 [635 600]]
============================== 

============ MultiLayerPerceptron ===========
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.918178153970175

 Precision of event Happening: 
 67.79661016949152

 Recall of event Happening: 
 48.582995951417004

 AUC: 
 0.6285490086495408

 F-Score:
 0.5660377358490566

 Confusion Matrix: 
 [[961 285]
 [635 600]]
============================== 

============ LightGbm ===========
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 4967, number of negative: 4956
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000115 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 10
[LightGBM] [Info] Number of data points in the train set: 9923, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500554 -> initscore=0.002217
[LightGBM] [Info] Start training from score 0.002217
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Prediction Vector: 
 [1. 0. 1. ... 0. 0. 0.]

 Accuracy: 
 62.918178153970175

 Precision of event Happening: 
 67.79661016949152

 Recall of event Happening: 
 48.582995951417004

 AUC: 
 0.6285490086495408

 F-Score:
 0.5660377358490566

 Confusion Matrix: 
 [[961 285]
 [635 600]]
============================== 

============ XgBoost ===========
[11:22:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Prediction Vector: 
 [1 0 1 ... 0 0 0]

 Accuracy: 
 62.87787182587666

 Precision of event Happening: 
 68.77990430622009

 Recall of event Happening: 
 46.558704453441294

 AUC: 
 0.628058369779245

 F-Score:
 0.5552873008208595

 Confusion Matrix: 
 [[985 261]
 [660 575]]
============================== 

============ LightGbm ===========
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 4967, number of negative: 4956
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000148 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 10
[LightGBM] [Info] Number of data points in the train set: 9923, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500554 -> initscore=0.002217
[LightGBM] [Info] Start training from score 0.002217
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Prediction Vector: 
 [1. 0. 1. ... 0. 0. 0.]

 Accuracy: 
 62.918178153970175

 Precision of event Happening: 
 67.79661016949152

 Recall of event Happening: 
 48.582995951417004

 AUC: 
 0.6285490086495408

 F-Score:
 0.5660377358490566

 Confusion Matrix: 
 [[961 285]
 [635 600]]
============================== 

In [33]:
## 4. Classification based Algrithms Results with both CV and RFFS and addressing class imbalancing
res_rffs_cv = template.MachineLearningwithRFFS_CV_clf(SAll_bal,'test_successful', threshold=5,
                                    algo_list=template.get_supported_algorithms_clf())
cert_degree2_Intermediate      17.925801
cert_degree2_A-Level           13.890728
studied_maths_Yes              12.359540
studied_maths_No               10.572414
cert_degree1_O-Level            5.970183
cert_degree1_Other boards       5.115042
discipline2_Science             4.391016
cert_degree1_Matriculation      3.401322
discipline2_Arts                3.286955
city_Others                     2.780194
gender_Female                   2.448693
gender_Male                     2.088421
city_Karachi-2                  1.685928
city_Karachi-3                  1.677672
Province_No_Sindh               1.584745
city_Hyderabad                  1.558019
city_Karachi-1                  1.500453
cert_degree1_Aga Khan Board     1.468577
Province_Sindh                  1.426194
cert_degree2_Aga Khan Board     1.134365
city_Lahore                     1.041228
city_Islamabad                  0.997018
cert_degree2_Other boards       0.958269
city_Peshawar                   0.393137
city_Quetta                     0.344085
dtype: float64
Selected Features =['cert_degree2_Intermediate', 'cert_degree2_A-Level', 'studied_maths_Yes', 'studied_maths_No', 'cert_degree1_O-Level', 'cert_degree1_Other boards']
(12404, 26)
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000128 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000120 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[11:23:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:23:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000135 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5581
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000134 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500045 -> initscore=0.000179
[LightGBM] [Info] Start training from score 0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11163, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499955 -> initscore=-0.000179
[LightGBM] [Info] Start training from score -0.000179
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 5582
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 11164, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
============ LogReg ===========
{'accuracy': 64.7187751813054, 'precision': 68.1284026873877, 'recall': 51.00994753519298, 'auc_val': 0.6471814451197341, 'f_score': 0.5738632122649998}
============================== 

============ KNN ===========
{'accuracy': 48.24150658937901, 'precision': 34.032006566298506, 'recall': 10.141473170224923, 'auc_val': 0.48243104254324454, 'f_score': 0.14346931389175263}
============================== 

============ GadientBoosting ===========
{'accuracy': 59.277962621195186, 'precision': 60.22295255654909, 'recall': 50.477585579969876, 'auc_val': 0.5927695963846035, 'f_score': 0.534969964303594}
============================== 

============ AdaBoost ===========
{'accuracy': 64.76712328767124, 'precision': 68.13234746056325, 'recall': 51.15510882551556, 'auc_val': 0.6476657056776272, 'f_score': 0.5747670762477152}
============================== 

============ SVM ===========
{'accuracy': 59.49567206467209, 'precision': 60.701079095888204, 'recall': 50.364708326840166, 'auc_val': 0.5949471456028259, 'f_score': 0.5354456286479738}
============================== 

============ DecisionTree ===========
{'accuracy': 59.51180109693015, 'precision': 60.74379953334549, 'recall': 50.3485792945821, 'auc_val': 0.5951084359254065, 'f_score': 0.5353943150190741}
============================== 

============ DeepNeuralNetwork ===========
{'accuracy': 60.00305424865483, 'precision': 61.19624897363066, 'recall': 50.7032881408758, 'auc_val': 0.6000141551088254, 'f_score': 0.5410048034571874}
============================== 

============ RandomForest ===========
{'accuracy': 59.26989160666476, 'precision': 60.19173003374539, 'recall': 50.47761155264662, 'auc_val': 0.5926890810866968, 'f_score': 0.5350181784570495}
============================== 

============ NaiveBayes ===========
{'accuracy': 61.50168958436224, 'precision': 60.54745670585887, 'recall': 54.895018440600495, 'auc_val': 0.6150116877045348, 'f_score': 0.5679304168024751}
============================== 

============ MultiLayerPerceptron ===========
{'accuracy': 59.72101063138469, 'precision': 60.81089095940111, 'recall': 51.00958391771856, 'auc_val': 0.5972127421952106, 'f_score': 0.5402761709719227}
============================== 

============ LightGbm ===========
{'accuracy': 59.60046528553975, 'precision': 60.81198659813466, 'recall': 51.02607656745104, 'auc_val': 0.5959985195574256, 'f_score': 0.5386670177586771}
============================== 

============ XgBoost ===========
{'accuracy': 59.51180109693015, 'precision': 60.74379953334549, 'recall': 50.3485792945821, 'auc_val': 0.5951084359254065, 'f_score': 0.5353943150190741}
============================== 

The results of aforementioned four scenarios indicate that the Random Forest is being successful in predicting the outcome variable.

The results indicate that:

• With the application of CV, the precision and accuracy increase in comparison to the benchmark category i.e., without CV and RFFS.

• The performance in terms all four criterions (precision, recall, AUC and accuracy) worsens a little bit with the application of features selection criterion in comparison to the benchmark category

• Selected Features are

'cert_degree2_Intermediate',

'studied_maths_No', 

'cert_degree2_A-Level',

'cert_degree1_O-Level', 

'studied_maths_Yes', 

'cert_degree1_Other boards'

• With the application of both CV and RFFS criterions, almost all 11 algorithms underperform than the CV only based scenario but outperform the rest of two scenarios.

• The best performing algorithm seems to be the Random Forest. Hence, in next section we carry out detailed analysis of the problem through Random Forest to construct the maximum depth tree and small depth tree and find out the most important predictors of the test success probability.

• Furthermore, the comparison between focusing the class imbalance issue and without addressing the class imbalance issue show that the overall accuracy is high in case of without addressing the class imbalance issue. However, the precision and recall is significantly misleading and worst in the scenario of without addressing the class imbalance issue. The results with addressing the class imbalance issue are reported here and results without addressing the class imbalance issue are reported in Appendix-A for reference.

Detailed Analysis of Test Successful Classification Problem through Random Forest

In [34]:
from sklearn.tree import export_graphviz
import pydot
from IPython.display import Image
#Full Tree with Random Forest without specifying the Maximum Depth of Tree (Considering all 26 features)
template.RF_all(SAll_bal,label_col='test_successful')
Image(filename = 'tree_all.png')
accuracy = 67.88132860367624 precision= 66.77253478523896 recall= 71.18671396323766 auc_val= 0.6788132860367623 f_score= 0.6890900577493366
Out[34]:

Random Forest without specifying the Maximum Depth of Tree (Considering all 26 features)

In this analysis (function RF_all), we use all features (26 features)

• n_estimators=1000

• random_state = 42 and

• no bound on tree length

The results show that accuracy, precision and recall of event happening (test successful) is reasonable. The tree is saved as "tree_all.png" and can be viewed in the folder. The tree is very large and cannot be interpreted easily. Therefore, the Random Forest algorithm with allowing maximum depth of tree to 5 is being run.

In [35]:
#Random Forest with allowing Maximum Depth of Tree to 5(Considering all 26 features)

template.RF_all_small(SAll_bal,label_col='test_successful')
Image(filename = 'small_treeall.png')
accuracy = 66.22057400838439 precision= 70.24144869215291 recall= 56.28829409867785 auc_val= 0.6622057400838438 f_score= 0.6249552452559971
Out[35]:

Random Forest with allowing Maximum Depth of Tree to 5 (Considering all 26 features) In the above analysis (function RF_all_small), we use all features (26 features)

• n_estimators=1000

• random_state = 42 and

• max_depth = 5

The results show that accuracy is almost similar, and the precision improves. However, the recall falls of event happening (test successful). The tree is saved as "small_treeall.png" and can be viewed in folder. The tree indicates that important role in predicting the test outcome is played by:

  1. cert_degree1_O-Level (9th and 10th curriculum)
  2. cert_degree2_A-Level (11th and 12th class)
  3. cert_degree2_Intermediate (11th and 12th class)
  4. Studied Mathematics- Yes
  5. Studied Mathematics-NO

With these attributes the prediction accuracy is about 67% and precision is 70%.

In [36]:
template.RF_imp(SAll_bal,label_col='test_successful')
Variable: cert_degree2_A-Level Importance: 0.15
Variable: cert_degree2_Intermediate Importance: 0.14
Variable: studied_maths_No     Importance: 0.11
Variable: studied_maths_Yes    Importance: 0.11
Variable: cert_degree1_O-Level Importance: 0.09
Variable: cert_degree1_Other boards Importance: 0.05
Variable: discipline2_Arts     Importance: 0.04
Variable: discipline2_Science  Importance: 0.04
Variable: city_Others          Importance: 0.03
Variable: cert_degree1_Matriculation Importance: 0.03
Variable: gender_Female        Importance: 0.02
Variable: gender_Male          Importance: 0.02
Variable: Province_Sindh       Importance: 0.02
Variable: city_Hyderabad       Importance: 0.02
Variable: city_Karachi-2       Importance: 0.02
Variable: city_Karachi-3       Importance: 0.02
Variable: Province_No_Sindh    Importance: 0.02
Variable: city_Islamabad       Importance: 0.01
Variable: city_Karachi-1       Importance: 0.01
Variable: city_Lahore          Importance: 0.01
Variable: cert_degree1_Aga Khan Board Importance: 0.01
Variable: cert_degree2_Aga Khan Board Importance: 0.01
Variable: cert_degree2_Other boards Importance: 0.01
Variable: city_Peshawar        Importance: 0.0
Variable: city_Quetta          Importance: 0.0
In [37]:
#Full Tree with Random Forest without specifying the Maximum Depth of Tree (Considering the top 6 features from above)
template.RF_all_imp(SAll_bal,label_col='test_successful')
Image(filename = 'tree_all_imp.png')
df is all cleaned up..
accuracy = 65.26926797807158 precision= 64.29218231210383 recall= 68.68752015478877 auc_val= 0.6526926797807159 f_score= 0.6641721234798876
Out[37]:
In [38]:
#Random Forest with allowing Maximum Depth of Tree to 5(Considering the top 6 features from above)
template.RFall_small_imp(SAll_bal, label_col='test_successful')
Image(filename = 'small_treeall_imp.png')
df is all cleaned up..
accuracy = 65.26926797807158 precision= 64.29218231210383 recall= 68.68752015478877 auc_val= 0.6526926797807159 f_score= 0.6641721234798876
Out[38]:

Overall Results of Random Forest Algorithm The Random Forest results show that even by considering the first six features, which are:

  1. cert_degree2_A-Level
  2. cert_degree2_Intermediate
  3. studied_maths_No
  4. studied_maths_Yes
  5. cert_degree1_O-Level
  6. cert_degree1_Other boards

the overall accuracy, precision and recall of event happening (means being successful in test) only fall by about 2%. Overall, the analysis suggests that we should consider the Random Forest algorithm to predict the outcome of entry test with above six features with allowing the tree depth to maximum level of 5. The precision and accuracy improve by allowing the tree depth further but in that case the tree becomes very complicated and difficult to interpret. Following conclusion can be drawn from this analysis:

  1. Curriculum (both at 9 and 10th classes and 11th and 12th classes level) is the most important variable.
  2. Mathematics background plays a significant role in predicting the outcome of entry test. The city variable which was assumed to capture the socio-economic status does not seem significant. The socio-economic status is an important indicator, however, in our case the city variable may not be the right proxy to represent the socio-economic status of the candidate.

Regression-based Prediction of Mathematics Score

In [39]:
#Setting the sample for analysis of Mathematics Score
S_math= template.cleaningup(df1, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['Eng_Score','test_successful'])
df is all cleaned up..

To analyze the prediction of Mathematics and select the best candidate ML algorithm, we have applied ten different machine learning regression algorithms considering the following four different scenarios:

• Without focusing the cross validation (CV) and features selection.

• Only cross validation (CV: cross_valid_kfold) is considered

• Only feature selection (random forest based algorithm is used) criterion is considered

• Both cross validation (CV: cross_valid_kfold)

For features selection, we run the random forest based algorithm to extract the top candidate features.

Here, we report the following result of best scenarios:

• without focusing the cross validation (CV) and features selection

Results of remaining scenarios are reported in Appendix-B.

In [40]:
## **1. Regrssion based Algrithms Results without CV and RFFS**
#withoutCV and RFFS 
results_without_cv_reg_rffs= template.run_algorithms_reg(S_math,'Math_Score')
============ LinearReg ===========
R-Squared Value:  0.2402995380873324
Adjusted R-Squared:  0.2378523648431864
MAE:  12.087517667667266
RMSE:  14.922773615625953
============================== 

============ RidgeReg ===========
R-Squared Value:  0.2401850772087837
Adjusted R-Squared:  0.23773753525932084
MAE:  12.089108874667195
RMSE:  14.923897749104757
============================== 

============ LassoReg ===========
R-Squared Value:  0.24001385441764803
Adjusted R-Squared:  0.23756576091944437
MAE:  12.090235858494612
RMSE:  14.925579189813675
============================== 

============ RandomForestReg ===========
R-Squared Value:  0.27017253878661396
Adjusted R-Squared:  0.26782159347926504
MAE:  11.77517456970479
RMSE:  14.626433995078552
============================== 

============ SupportVectorRegression ===========
R-Squared Value:  0.268951664281338
Adjusted R-Squared:  0.26659678625106265
MAE:  11.66914216837546
RMSE:  14.638662624189385
============================== 

============ DecisionTreeReg ===========
R-Squared Value:  0.26864135996448835
Adjusted R-Squared:  0.26628548237128036
MAE:  11.781973047061618
RMSE:  14.641769093391902
============================== 

============ DeepNeuralNetworkReg ===========
R-Squared Value:  0.27504449284944676
Adjusted R-Squared:  0.2727092412480083
MAE:  11.76247885782811
RMSE:  14.5775329605308
============================== 

============ GradientBoostingReg ===========
R-Squared Value:  0.277454956315514
Adjusted R-Squared:  0.27512746938185695
MAE:  11.740773258750009
RMSE:  14.553277769870318
============================== 

============ AdaBooostReg ===========
R-Squared Value:  0.2136301185683922
Adjusted R-Squared:  0.21109703687327686
MAE:  12.483506419868462
RMSE:  15.182447605520007
============================== 

============ VotingReg ===========
R-Squared Value:  0.2720484607746412
Adjusted R-Squared:  0.2697035582516888
MAE:  11.840735130351367
RMSE:  14.607624273100317
============================== 

The results indicate that: • With the application of CV and features selections based criterions, the R2 as well as RMSE worsen significantly. The benchmark scenario (without CV and RFFS) performs better in terms of both R2 as well as RMSE than the remaining three scenarios.

• The important features selected through Random Forest are 'cert_degree1_Matriculation' 'cert_degree2_A-Level' 'studied_maths_No' 'cert_degree1_Other boards' 'studied_maths_Yes'

• With the application of features selection criterion, the R2 falls about 2% and the RMSE increases about 3% in comparison to the benchmark scenario.

• The best performing algorithm seems to be the Gradient Boosting Regression and Random Forest which have R2 of about 28% and RMSE of 14%. The performance of all remaining algorithms is considerably worse.

Overall, the prediction performance of all ML algorithm is not encouraging. This may be due to the prediction of continuous variable (Mathematics score) only with the help of categorical variables. In my view, if we have the grades or percentage marks of the applicants in the last examination, i.e. A-Level / Intermediate, the prediction may improve significantly as it seems an important indicator of fitting and predicting the Mathematics score in the entry test.

The detailed results of all 4 scenarios are given in Appendix-B for reference.

Regression-based Prediction of English Score

In [41]:
#Setting the sample for analysis of English Score
S_eng= template.cleaningup(df1, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['Math_Score','test_successful'])
df is all cleaned up..

To analyze the prediction of English score in the entry test and select the best candidate ML algorithm, we have applied ten different machine learning regression algorithms considering the four different scenarios, which are

a. without focusing the cross validation (CV) and features selection.

b. Only cross validation (CV: cross_valid_kfold) is considered

c. Only feature selection (random forest based algorithm is used) criterion is considered

d. Both cross validation (CV: cross_valid_kfold)

For features selection, we run the random forest based algorithm to extract the top candidate features.

Here, we report the result of best scenarios which is

• without focusing the cross validation (CV) and features selection

and report the results of remaining scenarios in Appendix-C for reference.

In [42]:
## **1. Regrssion based Algrithms Results without CV and RFFS**
#without CV and RFFS 
results_withoutCV_RFFS_Eng= template.run_algorithms_reg(S_eng,"Eng_Score")
============ LinearReg ===========
R-Squared Value:  0.1619175121597779
Adjusted R-Squared:  0.15921785203917427
MAE:  12.901545685116218
RMSE:  16.143095230919723
============================== 

============ RidgeReg ===========
R-Squared Value:  0.1615841133633732
Adjusted R-Squared:  0.15888337928710528
MAE:  12.899228458413164
RMSE:  16.14630586557721
============================== 

============ LassoReg ===========
R-Squared Value:  0.1617707002308051
Adjusted R-Squared:  0.1590705671945688
MAE:  12.896473072133317
RMSE:  16.14450911034221
============================== 

============ RandomForestReg ===========
R-Squared Value:  0.16485217397680463
Adjusted R-Squared:  0.16216196708973085
MAE:  12.860896996234725
RMSE:  16.11480680421095
============================== 

============ SupportVectorRegression ===========
R-Squared Value:  0.16823407918232902
Adjusted R-Squared:  0.165554766204563
MAE:  12.811268299953362
RMSE:  16.082145500391903
============================== 

============ DecisionTreeReg ===========
R-Squared Value:  0.15815266635889036
Adjusted R-Squared:  0.15544087878756863
MAE:  12.897246524804086
RMSE:  16.179313719587846
============================== 

============ DeepNeuralNetworkReg ===========
R-Squared Value:  0.16994819220268298
Adjusted R-Squared:  0.16727440078470424
MAE:  12.853866563970735
RMSE:  16.06556581651595
============================== 

============ GradientBoostingReg ===========
R-Squared Value:  0.17082286050072892
Adjusted R-Squared:  0.16815188659434033
MAE:  12.845414572738852
RMSE:  16.057099028799072
============================== 

============ AdaBooostReg ===========
R-Squared Value:  0.12884926889875115
Adjusted R-Squared:  0.1260430882161676
MAE:  13.255650173366567
RMSE:  16.458493431867346
============================== 

============ VotingReg ===========
R-Squared Value:  0.16908183045657077
Adjusted R-Squared:  0.16640524828435255
MAE:  12.880829460737855
RMSE:  16.073947800359743
============================== 

Results

The results indicate that

• With the application of CV and features selections based criterions, the R2 as well as RMSE worsen significantly. The benchmark scenario (without CV and RFFS) performs better in terms of both R2 as well as RMSE than the other three scenarios. However, the results are not as good as were for the Mathematics score analysis. The R2 falls from 27% to 16%.

• The important features selected through Random Forest are

'cert_degree1_Matriculation'

'cert_degree2_A-Level'

'studied_maths_No'

'cert_degree1_Other boards'

'studied_maths_Yes'

• with the application of features selection criterion, the R2 falls about 2% and the RMSE increases about 3% in comparison to the benchmark scenario.

• The best performing algorithm seems to be the Deep Neural Network based Regression model followed by Gradient Boosting which have R2 of about 17% and RMSE of 16%. The performance of all remaining algorithms is considerably worse.

Overall, the prediction performance of all ML algorithm is not encouraging. This may be due to the prediction of continuous variable (English score) only with the help of categorical variables. In my view, if we have the grades or percentage marks of the applicants in the last examination, i.e. A-Level / Intermediate, the prediction may improve significantly as it seems an important indicator of fitting and predicting the English score in the entry test.

The detailed results of all 4 scenarios are given in Appendix-C for reference.

Conclusion:

The purpose of this project was to gauge the prediction capability of different machine learnings models. We used the test scores of 38,931 candidates from year 2014 to 2020 and tried to predict:

1- Test successful (classification problem)

2- English score (regression problem)

3- Mathematics score (regression problem)

The results suggest that for classification problem, Random Forest model has better prediction power than any other algorithm. For regression problems, Gradient Boosting is more suitable for predicting mathematics score while for predicting English score, Deep Neural Network came out to be more suitable. The performance of Random Forest model is close to Deep Neural Network and Gradient Boosting models in the regression-based problems. Most important features for predicting the test performance of a candidates have been identified as curriculum of the candidate (A-Level/Intermediate) and whether the candidate has prior mathematics background.

Unavailability of prior academic scores in A-Level / Intermediate has been identified as a limitation of this study. The availability of this data may significantly improve the prediction power of the models used in the study.

Appendix-A: Classification-based Prediction of Success Without Addressing Class Imbalance

In [43]:
## **1. Classification based Algrithms Results without CV and RFFS**
#without REG, CV and RFFS and addressing class imbalancing
results_without_cvRFFS_cls= template.run_algorithms_clf(S_all,'test_successful')
============ LogReg ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.39707204314884

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.4999239312338354

 F-Score:
 0.0

 Confusion Matrix: 
 [[6572    1]
 [1214    0]]
============================== 

============ KNN ===========
Prediction Vector: 
 [0 0 0 ... 0 0 1]

 Accuracy: 
 82.92025170155387

 Precision of event Happening: 
 30.79470198675497

 Recall of event Happening: 
 7.660626029654035

 AUC: 
 0.5224047580198661

 F-Score:
 0.12269129287598944

 Confusion Matrix: 
 [[6364  209]
 [1121   93]]
============================== 

============ GadientBoosting ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ AdaBoost ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.39707204314884

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.4999239312338354

 F-Score:
 0.0

 Confusion Matrix: 
 [[6572    1]
 [1214    0]]
============================== 

============ SVM ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ DecisionTree ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.24296905098241

 Precision of event Happening: 
 11.76470588235294

 Recall of event Happening: 
 0.16474464579901155

 AUC: 
 0.49968269173652585

 F-Score:
 0.0032493907392363935

 Confusion Matrix: 
 [[6558   15]
 [1212    2]]
============================== 

============ DeepNeuralNetwork ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.3713882111211

 Precision of event Happening: 
 20.0

 Recall of event Happening: 
 0.08237232289950577

 AUC: 
 0.5001075865498391

 F-Score:
 0.0016406890894175553

 Confusion Matrix: 
 [[6569    4]
 [1213    1]]
============================== 

============ RandomForest ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.23012713496854

 Precision of event Happening: 
 11.11111111111111

 Recall of event Happening: 
 0.16474464579901155

 AUC: 
 0.49960662297036124

 F-Score:
 0.0032467532467532465

 Confusion Matrix: 
 [[6557   16]
 [1212    2]]
============================== 

============ NaiveBayes ===========
Prediction Vector: 
 [0 0 0 ... 1 0 1]

 Accuracy: 
 73.73828175163734

 Precision of event Happening: 
 28.43798650752465

 Recall of event Happening: 
 45.14003294892916

 AUC: 
 0.6208013362036447

 F-Score:
 0.34893346068131165

 Confusion Matrix: 
 [[5194 1379]
 [ 666  548]]
============================== 

============ MultiLayerPerceptron ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.3713882111211

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.49977179370150615

 F-Score:
 0.0

 Confusion Matrix: 
 [[6570    3]
 [1214    0]]
============================== 

============ LightGbm ===========
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 4988, number of negative: 26156
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001041 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 31144, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.160159 -> initscore=-1.657044
[LightGBM] [Info] Start training from score -1.657044
Prediction Vector: 
 [0. 0. 0. ... 0. 0. 0.]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ XgBoost ===========
[11:32:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ LightGbm ===========
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 4988, number of negative: 26156
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002246 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 31144, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.160159 -> initscore=-1.657044
[LightGBM] [Info] Start training from score -1.657044
Prediction Vector: 
 [0. 0. 0. ... 0. 0. 0.]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

In [44]:
## **2. Classification based Algrithms Results with CV only**

#with CV only and with addressing class imbalancing
results_cv_clf = template.run_algorithms_cv_clf(S_all,'test_successful')
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002927 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35037, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159289 -> initscore=-1.663530
[LightGBM] [Info] Start training from score -1.663530
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002180 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001393 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001705 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001267 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001245 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001197 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001575 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 29457
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001254 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159284 -> initscore=-1.663564
[LightGBM] [Info] Start training from score -1.663564
[11:42:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:42:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:42:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:42:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:43:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:43:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:43:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:43:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:43:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:43:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001194 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35037, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159289 -> initscore=-1.663530
[LightGBM] [Info] Start training from score -1.663530
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001262 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001266 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001257 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001203 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001245 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001259 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001226 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001214 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 5581, number of negative: 29457
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001226 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 50
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 25
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159284 -> initscore=-1.663564
[LightGBM] [Info] Start training from score -1.663564
============ LogReg ===========
{'accuracy': 84.06411505195938, 'precision': 3.333333333333333, 'recall': 0.0322061191626409, 'auc_val': 0.5000999245768704, 'f_score': 0.0006379585326953748}
============================== 

============ KNN ===========
{'accuracy': 80.8096650896853, 'precision': 28.387229626034646, 'recall': 16.28845254791959, 'auc_val': 0.5466266649053506, 'f_score': 0.18227350550654436}
============================== 

============ GadientBoosting ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ AdaBoost ===========
{'accuracy': 84.06668310537489, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.4999847234952643, 'f_score': 0.0}
============================== 

============ SVM ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ DecisionTree ===========
{'accuracy': 83.9947657358743, 'precision': 35.69047619047619, 'recall': 0.225728533582671, 'auc_val': 0.5004717342888438, 'f_score': 0.0044564262225685655}
============================== 

============ DeepNeuralNetwork ===========
{'accuracy': 84.04613274111765, 'precision': 1.0, 'recall': 0.016129032258064516, 'auc_val': 0.4999278754450748, 'f_score': 0.0003174603174603174}
============================== 

============ RandomForest ===========
{'accuracy': 84.0127460677383, 'precision': 41.76877289377289, 'recall': 0.3385538413588904, 'auc_val': 0.5010358608277249, 'f_score': 0.006684308944703306}
============================== 

============ NaiveBayes ===========
{'accuracy': 75.10999751836195, 'precision': 33.292227412923914, 'recall': 42.94099007843748, 'auc_val': 0.6207380819187847, 'f_score': 0.3300230384443415}
============================== 

============ MultiLayerPerceptron ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ LightGbm ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ XgBoost ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

In [45]:
## **3. Classification based Algrithms Results with RFFS only**

#with RFFS only and without addressing class imbalancing
res_rffs_clf = template.MachineLearningwithRFFS_clf(S_all,'test_successful', threshold=5,
                                    algo_list=template.get_supported_algorithms_clf())
studied_maths_No               15.322639
cert_degree1_O-Level           14.020934
cert_degree2_A-Level           13.256182
cert_degree2_Intermediate      10.393659
studied_maths_Yes              10.209890
discipline2_Science             7.143059
discipline2_Arts                4.357948
cert_degree1_Other boards       3.465455
city_Others                     2.065979
gender_Male                     2.052568
gender_Female                   2.052412
cert_degree1_Matriculation      1.799521
city_Karachi-2                  1.624144
city_Karachi-3                  1.566775
Province_No_Sindh               1.517475
city_Karachi-1                  1.419193
cert_degree1_Aga Khan Board     1.183820
Province_Sindh                  1.153186
city_Hyderabad                  1.142658
city_Islamabad                  1.097222
city_Lahore                     0.931740
cert_degree2_Other boards       0.706921
cert_degree2_Aga Khan Board     0.682874
city_Quetta                     0.435067
city_Peshawar                   0.398681
dtype: float64
Selected Features =['studied_maths_No', 'cert_degree1_O-Level', 'cert_degree2_A-Level', 'cert_degree2_Intermediate', 'studied_maths_Yes', 'discipline2_Science']
(38931, 26)
============ LogReg ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ KNN ===========
Prediction Vector: 
 [0 0 0 ... 0 0 1]

 Accuracy: 
 83.16424810581738

 Precision of event Happening: 
 40.831758034026464

 Recall of event Happening: 
 17.792421746293247

 AUC: 
 0.5651525849219424

 F-Score:
 0.24784853700516357

 Confusion Matrix: 
 [[6260  313]
 [ 998  216]]
============================== 

============ GadientBoosting ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ AdaBoost ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ SVM ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ DecisionTree ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ DeepNeuralNetwork ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ RandomForest ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ NaiveBayes ===========
Prediction Vector: 
 [0 0 0 ... 1 0 1]

 Accuracy: 
 78.2714781045332

 Precision of event Happening: 
 33.31005586592179

 Recall of event Happening: 
 39.29159802306425

 AUC: 
 0.623812318428116

 F-Score:
 0.36054421768707484

 Confusion Matrix: 
 [[5618  955]
 [ 737  477]]
============================== 

============ MultiLayerPerceptron ===========
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ LightGbm ===========
[LightGBM] [Info] Number of positive: 4988, number of negative: 26156
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 31144, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.160159 -> initscore=-1.657044
[LightGBM] [Info] Start training from score -1.657044
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Prediction Vector: 
 [0. 0. 0. ... 0. 0. 0.]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ XgBoost ===========
[11:43:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Prediction Vector: 
 [0 0 0 ... 0 0 0]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

============ LightGbm ===========
[LightGBM] [Info] Number of positive: 4988, number of negative: 26156
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000633 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 31144, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.160159 -> initscore=-1.657044
[LightGBM] [Info] Start training from score -1.657044
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Prediction Vector: 
 [0. 0. 0. ... 0. 0. 0.]

 Accuracy: 
 84.40991395916271

 Precision of event Happening: 
 0.0

 Recall of event Happening: 
 0.0

 AUC: 
 0.5

 F-Score:
 0.0

 Confusion Matrix: 
 [[6573    0]
 [1214    0]]
============================== 

In [46]:
## **4. Classification based Algrithms Results with both CV and RFFS**

#with CV and RFFS and without addressing class imbalancing
res_rffs_cv = template.MachineLearningwithRFFS_CV_clf(S_all,'test_successful', threshold=5,
                                    algo_list=template.get_supported_algorithms_clf())
cert_degree2_A-Level           18.147152
cert_degree2_Intermediate      12.266995
cert_degree1_O-Level           11.781272
studied_maths_Yes               9.712526
studied_maths_No                9.216365
discipline2_Arts                5.631796
cert_degree1_Other boards       4.815824
discipline2_Science             4.353571
cert_degree1_Matriculation      3.940280
gender_Male                     2.272477
city_Others                     1.960754
gender_Female                   1.836935
cert_degree1_Aga Khan Board     1.641374
city_Karachi-2                  1.517454
city_Karachi-3                  1.359385
Province_No_Sindh               1.344822
city_Hyderabad                  1.292613
city_Karachi-1                  1.241562
Province_Sindh                  1.096349
cert_degree2_Aga Khan Board     1.012247
cert_degree2_Other boards       0.985839
city_Lahore                     0.883983
city_Islamabad                  0.778803
city_Quetta                     0.545144
city_Peshawar                   0.364477
dtype: float64
Selected Features =['cert_degree2_A-Level', 'cert_degree2_Intermediate', 'cert_degree1_O-Level', 'studied_maths_Yes', 'studied_maths_No', 'discipline2_Arts']
(38931, 26)
[LightGBM] [Info] Number of positive: 5581, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35037, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159289 -> initscore=-1.663530
[LightGBM] [Info] Start training from score -1.663530
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000648 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000622 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000925 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000604 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5581, number of negative: 29457
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003770 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159284 -> initscore=-1.663564
[LightGBM] [Info] Start training from score -1.663564
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[11:49:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[11:49:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[LightGBM] [Info] Number of positive: 5581, number of negative: 29456
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35037, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159289 -> initscore=-1.663530
[LightGBM] [Info] Start training from score -1.663530
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000590 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000639 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000704 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5582, number of negative: 29456
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000577 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159313 -> initscore=-1.663350
[LightGBM] [Info] Start training from score -1.663350
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 5581, number of negative: 29457
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000678 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 35038, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.159284 -> initscore=-1.663564
[LightGBM] [Info] Start training from score -1.663564
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
============ LogReg ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ KNN ===========
{'accuracy': 80.71194317009274, 'precision': 6.656706766525927, 'recall': 8.85483870967742, 'auc_val': 0.5159210007588972, 'f_score': 0.07028938698372802}
============================== 

============ GadientBoosting ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ AdaBoost ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ SVM ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ DecisionTree ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ DeepNeuralNetwork ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ RandomForest ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ NaiveBayes ===========
{'accuracy': 78.06652689806722, 'precision': 30.268572497520864, 'recall': 39.10435821515765, 'auc_val': 0.6227750755243064, 'f_score': 0.2938423775169174}
============================== 

============ MultiLayerPerceptron ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ LightGbm ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

============ XgBoost ===========
{'accuracy': 84.0692511587904, 'precision': 0.0, 'recall': 0.0, 'auc_val': 0.5, 'f_score': 0.0}
============================== 

Appendix-B: Regression-based Prediction of Mathematics Score

In [47]:
## **1. Regrssion based Algrithms Results without CV and RFFS**
#withoutCV and RFFS 
results_without_cv_reg_rffs= template.run_algorithms_reg(S_math,'Math_Score')
============ LinearReg ===========
R-Squared Value:  0.2402995380873324
Adjusted R-Squared:  0.2378523648431864
MAE:  12.087517667667266
RMSE:  14.922773615625953
============================== 

============ RidgeReg ===========
R-Squared Value:  0.2401850772087837
Adjusted R-Squared:  0.23773753525932084
MAE:  12.089108874667195
RMSE:  14.923897749104757
============================== 

============ LassoReg ===========
R-Squared Value:  0.24001385441764803
Adjusted R-Squared:  0.23756576091944437
MAE:  12.090235858494612
RMSE:  14.925579189813675
============================== 

============ RandomForestReg ===========
R-Squared Value:  0.27043188371748983
Adjusted R-Squared:  0.2680817738209479
MAE:  11.77316662685523
RMSE:  14.623835005068985
============================== 

============ SupportVectorRegression ===========
R-Squared Value:  0.268951664281338
Adjusted R-Squared:  0.26659678625106265
MAE:  11.66914216837546
RMSE:  14.638662624189385
============================== 

============ DecisionTreeReg ===========
R-Squared Value:  0.2685229367140538
Adjusted R-Squared:  0.26616667765180047
MAE:  11.783285293832963
RMSE:  14.642954459646306
============================== 

============ DeepNeuralNetworkReg ===========
R-Squared Value:  0.2754937278179337
Adjusted R-Squared:  0.27315992330761907
MAE:  11.73617591510554
RMSE:  14.57301561268801
============================== 

============ GradientBoostingReg ===========
R-Squared Value:  0.277454956315514
Adjusted R-Squared:  0.27512746938185695
MAE:  11.740773258750009
RMSE:  14.553277769870318
============================== 

============ AdaBooostReg ===========
R-Squared Value:  0.2136301185683922
Adjusted R-Squared:  0.21109703687327686
MAE:  12.483506419868462
RMSE:  15.182447605520007
============================== 

============ VotingReg ===========
R-Squared Value:  0.2720482621849014
Adjusted R-Squared:  0.26970335902224485
MAE:  11.841620520438571
RMSE:  14.607626265625884
============================== 

In [48]:
## **2. Regrssion based Algrithms Results with CV only**
#with CV only 
results_cv = template.run_algorithms_cv_reg(S_math,'Math_Score')
============ LinearReg ===========
{'r2': 0.119817631213965, 'r2_adjusted': 0.11412743639919756, 'mae': 12.132393954695818, 'rmse': 14.938626087459344}
============================== 

============ RidgeReg ===========
{'r2': 0.11953099394379105, 'r2_adjusted': 0.11383894616609976, 'mae': 12.136241560133692, 'rmse': 14.941051235164633}
============================== 

============ LassoReg ===========
{'r2': 0.11983724806078644, 'r2_adjusted': 0.11414717953812437, 'mae': 12.138256978376443, 'rmse': 14.939172528340602}
============================== 

============ RandomForestReg ===========
{'r2': 0.1447826024563575, 'r2_adjusted': 0.1392538110408113, 'mae': 11.879836967105843, 'rmse': 14.716643427014642}
============================== 

============ SupportVectorRegression ===========
{'r2': 0.1471053472837443, 'r2_adjusted': 0.14159156572092896, 'mae': 11.763965672436814, 'rmse': 14.696301186899149}
============================== 

============ DecisionTreeReg ===========
{'r2': 0.14182108744639343, 'r2_adjusted': 0.13627315153188446, 'mae': 11.897582497203109, 'rmse': 14.74263312023071}
============================== 

============ DeepNeuralNetworkReg ===========
{'r2': 0.1514410531403561, 'r2_adjusted': 0.1459552992478173, 'mae': 11.85841333098235, 'rmse': 14.655006087897926}
============================== 

============ GradientBoostingReg ===========
{'r2': 0.1592226413369308, 'r2_adjusted': 0.1537871881628014, 'mae': 11.808568852128847, 'rmse': 14.589841839858485}
============================== 

============ AdaBooostReg ===========
{'r2': 0.09673898549333208, 'r2_adjusted': 0.0908995829287338, 'mae': 12.487887144571532, 'rmse': 15.137655260738418}
============================== 

============ VotingReg ===========
{'r2': 0.1538107999577179, 'r2_adjusted': 0.1483403621499137, 'mae': 11.907447375162198, 'rmse': 14.640497970008274}
============================== 

In [49]:
## **3. Regrssion based Algrithms Results with RFFS only**
#with RFFS only 
res_rffs = template.MachineLearningwithRFFS_reg(S_math,'Math_Score', threshold=5,
                                    algo_list=template.get_supported_algorithms_reg())
cert_degree1_Matriculation     35.828490
cert_degree2_A-Level           14.021842
studied_maths_No               12.953509
cert_degree1_Other boards      10.789181
studied_maths_Yes               9.585195
discipline2_Arts                3.416340
discipline2_Science             2.746731
city_Others                     1.665540
gender_Female                   1.346942
gender_Male                     1.047747
Province_No_Sindh               0.931605
city_Hyderabad                  0.671787
Province_Sindh                  0.587022
city_Karachi-3                  0.584834
cert_degree2_Intermediate       0.550001
city_Karachi-2                  0.539999
city_Karachi-1                  0.487054
city_Islamabad                  0.430850
cert_degree2_Aga Khan Board     0.354418
city_Lahore                     0.321752
cert_degree2_Other boards       0.291224
cert_degree1_Aga Khan Board     0.269776
cert_degree1_O-Level            0.213173
city_Quetta                     0.185110
city_Peshawar                   0.179879
dtype: float64
Selected Features =['cert_degree1_Matriculation', 'cert_degree2_A-Level', 'studied_maths_No', 'cert_degree1_Other boards', 'studied_maths_Yes']
(38931, 26)
============ LinearReg ===========
R-Squared Value:  0.23012710566737582
Adjusted R-Squared:  0.22963239233083
MAE:  12.176264294156125
RMSE:  15.022349785396068
============================== 

============ RidgeReg ===========
R-Squared Value:  0.23014280897869424
Adjusted R-Squared:  0.2296481057329537
MAE:  12.176604186819606
RMSE:  15.022196577093702
============================== 

============ LassoReg ===========
R-Squared Value:  0.23014235133500172
Adjusted R-Squared:  0.22964764779518354
MAE:  12.177938412274251
RMSE:  15.022201042085761
============================== 

============ RandomForestReg ===========
R-Squared Value:  0.25317877049628046
Adjusted R-Squared:  0.25269886995039703
MAE:  11.973952473768014
RMSE:  14.795739721616645
============================== 

============ SupportVectorRegression ===========
R-Squared Value:  0.244454941760189
Adjusted R-Squared:  0.24396943536111448
MAE:  11.910173361309912
RMSE:  14.881905418379494
============================== 

============ DecisionTreeReg ===========
R-Squared Value:  0.2531613014280315
Adjusted R-Squared:  0.2526813896566834
MAE:  11.973962411497725
RMSE:  14.795912765891048
============================== 

============ DeepNeuralNetworkReg ===========
R-Squared Value:  0.25243704792757093
Adjusted R-Squared:  0.2519566707574947
MAE:  11.987808361700452
RMSE:  14.80308526171276
============================== 

============ GradientBoostingReg ===========
R-Squared Value:  0.2524005378117753
Adjusted R-Squared:  0.25192013718063
MAE:  11.980804753126675
RMSE:  14.80344674013863
============================== 

============ AdaBooostReg ===========
R-Squared Value:  0.1924925334288794
Adjusted R-Squared:  0.19197363645768606
MAE:  12.65885810270255
RMSE:  15.38514624160653
============================== 

============ VotingReg ===========
R-Squared Value:  0.2480773023259889
Adjusted R-Squared:  0.24759412362294686
MAE:  12.081574697630113
RMSE:  14.84618789542
============================== 

In [50]:
## **4. Regrssion based Algrithms Results with both CV and RFFS** 
#with CV and RFFS both
res_rffs_cv = template.MachineLearningwithRFFS_CV_reg(S_math,"Math_Score", threshold=5,
                                                      algo_list=template.get_supported_algorithms_reg())
cert_degree1_Matriculation     36.577758
cert_degree2_A-Level           14.112391
studied_maths_No               12.888915
cert_degree1_Other boards      10.403025
studied_maths_Yes               9.328163
discipline2_Arts                3.172560
discipline2_Science             2.689242
city_Others                     1.788182
gender_Male                     1.194272
gender_Female                   1.157825
Province_No_Sindh               0.836262
Province_Sindh                  0.735462
city_Karachi-3                  0.692371
city_Hyderabad                  0.617070
cert_degree2_Intermediate       0.608585
city_Karachi-1                  0.549428
city_Karachi-2                  0.526237
cert_degree2_Aga Khan Board     0.400363
city_Islamabad                  0.377329
cert_degree2_Other boards       0.293337
cert_degree1_Aga Khan Board     0.237166
city_Lahore                     0.226833
cert_degree1_O-Level            0.207900
city_Quetta                     0.195749
city_Peshawar                   0.183576
dtype: float64
Selected Features =['cert_degree1_Matriculation', 'cert_degree2_A-Level', 'studied_maths_No', 'cert_degree1_Other boards', 'studied_maths_Yes']
(38931, 26)
============ LinearReg ===========
{'r2': 0.11067959704215165, 'r2_adjusted': 0.10953565872107607, 'mae': 12.21323892447696, 'rmse': 15.016267472367272}
============================== 

============ RidgeReg ===========
{'r2': 0.11008827402572133, 'r2_adjusted': 0.10894357503236995, 'mae': 12.213222262830923, 'rmse': 15.018987497713406}
============================== 

============ LassoReg ===========
{'r2': 0.11018591502122473, 'r2_adjusted': 0.10904134162207571, 'mae': 12.21520411386183, 'rmse': 15.018690331334108}
============================== 

============ RandomForestReg ===========
{'r2': 0.13679350797882012, 'r2_adjusted': 0.13568315953425755, 'mae': 11.990635725548577, 'rmse': 14.78223165990487}
============================== 

============ SupportVectorRegression ===========
{'r2': 0.12650972139656141, 'r2_adjusted': 0.12538614553826272, 'mae': 11.934004905862933, 'rmse': 14.870419571334732}
============================== 

============ DecisionTreeReg ===========
{'r2': 0.13691925208786704, 'r2_adjusted': 0.13580906537732754, 'mae': 11.990011609126432, 'rmse': 14.7811458714656}
============================== 

============ DeepNeuralNetworkReg ===========
{'r2': 0.13578207100251669, 'r2_adjusted': 0.13467042151630473, 'mae': 11.994658406709913, 'rmse': 14.790540065967093}
============================== 

============ GradientBoostingReg ===========
{'r2': 0.13633711935876505, 'r2_adjusted': 0.13522618395552413, 'mae': 11.994752754275172, 'rmse': 14.786685390573524}
============================== 

============ AdaBooostReg ===========
{'r2': 0.08822500471289468, 'r2_adjusted': 0.08705218183362287, 'mae': 12.551444836285475, 'rmse': 15.214888959937934}
============================== 

============ VotingReg ===========
{'r2': 0.1348119705653919, 'r2_adjusted': 0.1336990729799059, 'mae': 12.068629251590659, 'rmse': 14.802836562031198}
============================== 

Appendix-C: Regression-based Prediction of English Score

In [51]:
## **1. Regrssion based Algrithms Results without CV and RFFS**
#without CV and RFFS 
results_withoutCV_RFFS_Eng= template.run_algorithms_reg(S_eng,"Eng_Score")
============ LinearReg ===========
R-Squared Value:  0.1619175121597779
Adjusted R-Squared:  0.15921785203917427
MAE:  12.901545685116218
RMSE:  16.143095230919723
============================== 

============ RidgeReg ===========
R-Squared Value:  0.1615841133633732
Adjusted R-Squared:  0.15888337928710528
MAE:  12.899228458413164
RMSE:  16.14630586557721
============================== 

============ LassoReg ===========
R-Squared Value:  0.1617707002308051
Adjusted R-Squared:  0.1590705671945688
MAE:  12.896473072133317
RMSE:  16.14450911034221
============================== 

============ RandomForestReg ===========
R-Squared Value:  0.16457927960802743
Adjusted R-Squared:  0.16188819366423157
MAE:  12.86563149671125
RMSE:  16.117439440438524
============================== 

============ SupportVectorRegression ===========
R-Squared Value:  0.16823407918232902
Adjusted R-Squared:  0.165554766204563
MAE:  12.811268299953362
RMSE:  16.082145500391903
============================== 

============ DecisionTreeReg ===========
R-Squared Value:  0.15685087299366318
Adjusted R-Squared:  0.15413489204080166
MAE:  12.904691296153171
RMSE:  16.191818354532597
============================== 

============ DeepNeuralNetworkReg ===========
R-Squared Value:  0.17164375616477645
Adjusted R-Squared:  0.16897542655572084
MAE:  12.818936685455407
RMSE:  16.049148696869736
============================== 

============ GradientBoostingReg ===========
R-Squared Value:  0.17082286050072892
Adjusted R-Squared:  0.16815188659434033
MAE:  12.84541457273885
RMSE:  16.057099028799072
============================== 

============ AdaBooostReg ===========
R-Squared Value:  0.12884926889875115
Adjusted R-Squared:  0.1260430882161676
MAE:  13.255650173366567
RMSE:  16.458493431867346
============================== 

============ VotingReg ===========
R-Squared Value:  0.16889705100936792
Adjusted R-Squared:  0.16621987361924218
MAE:  12.881908201772177
RMSE:  16.075734961954947
============================== 

In [52]:
## **2. Regrssion based Algrithms Results with CV only**
#with CV only
results_cv = template.run_algorithms_cv_reg(S_eng,"Eng_Score")
============ LinearReg ===========
{'r2': -0.06374830432619602, 'r2_adjusted': -0.07062521875831743, 'mae': 13.409836269070501, 'rmse': 16.362843753419433}
============================== 

============ RidgeReg ===========
{'r2': -0.06250295856305624, 'r2_adjusted': -0.06937182162892754, 'mae': 13.397963645323424, 'rmse': 16.352274604557977}
============================== 

============ LassoReg ===========
{'r2': -0.06232480409897763, 'r2_adjusted': -0.06919251466851953, 'mae': 13.401346580274708, 'rmse': 16.353583194602752}
============================== 

============ RandomForestReg ===========
{'r2': -0.07296821264810238, 'r2_adjusted': -0.07990472823592908, 'mae': 13.441739909788396, 'rmse': 16.44533313249342}
============================== 

============ SupportVectorRegression ===========
{'r2': -0.07478343530664137, 'r2_adjusted': -0.08173169022114213, 'mae': 13.451444364384566, 'rmse': 16.450021439277187}
============================== 

============ DecisionTreeReg ===========
{'r2': -0.08412468446109278, 'r2_adjusted': -0.0911333177454846, 'mae': 13.506112153008491, 'rmse': 16.539302129769684}
============================== 

============ DeepNeuralNetworkReg ===========
{'r2': -0.06485211897436063, 'r2_adjusted': -0.07173616989778976, 'mae': 13.40515312002658, 'rmse': 16.379945976186377}
============================== 

============ GradientBoostingReg ===========
{'r2': -0.05718594615583376, 'r2_adjusted': -0.06402043411460799, 'mae': 13.378779806336771, 'rmse': 16.320852125331772}
============================== 

============ AdaBooostReg ===========
{'r2': -0.07938407612566692, 'r2_adjusted': -0.08636205715270387, 'mae': 13.583447222754819, 'rmse': 16.49849737460031}
============================== 

============ VotingReg ===========
{'r2': -0.05726182376812455, 'r2_adjusted': -0.06409680010269361, 'mae': 13.38210139630279, 'rmse': 16.32419860152027}
============================== 

In [53]:
## **3. Regrssion based Algrithms Results with RFFS only**
##with RFFS only English 
res_rffs = template.MachineLearningwithRFFS_reg(S_eng,"Eng_Score", threshold=5,
                                    algo_list=template.get_supported_algorithms_reg())
cert_degree1_Matriculation     48.174467
cert_degree2_A-Level            7.748784
cert_degree1_Other boards       7.344466
discipline2_Science             6.333579
city_Others                     3.842816
gender_Female                   3.755714
studied_maths_Yes               2.269141
Province_Sindh                  1.770020
gender_Male                     1.684217
studied_maths_No                1.616758
Province_No_Sindh               1.615782
cert_degree2_Intermediate       1.586827
city_Karachi-1                  1.383760
discipline2_Arts                1.374081
city_Karachi-2                  1.249481
city_Hyderabad                  1.216204
city_Islamabad                  1.215400
city_Karachi-3                  1.154083
cert_degree1_O-Level            0.965658
cert_degree2_Other boards       0.842930
cert_degree1_Aga Khan Board     0.841214
city_Lahore                     0.751161
cert_degree2_Aga Khan Board     0.533253
city_Quetta                     0.424958
city_Peshawar                   0.305246
dtype: float64
Selected Features =['cert_degree1_Matriculation', 'cert_degree2_A-Level', 'cert_degree1_Other boards', 'discipline2_Science']
(38931, 26)
============ LinearReg ===========
R-Squared Value:  0.1395245039419114
Adjusted R-Squared:  0.13908221378716557
MAE:  13.10569502865673
RMSE:  16.357339944616015
============================== 

============ RidgeReg ===========
R-Squared Value:  0.13952450694496288
Adjusted R-Squared:  0.13908221679176058
MAE:  13.105695255109184
RMSE:  16.357339916072533
============================== 

============ LassoReg ===========
R-Squared Value:  0.1395200797119257
Adjusted R-Squared:  0.13907778728309605
MAE:  13.106836071250843
RMSE:  16.3573819960987
============================== 

============ RandomForestReg ===========
R-Squared Value:  0.1425370452589877
Adjusted R-Squared:  0.14209630357060887
MAE:  13.096607030437154
RMSE:  16.328681157476854
============================== 

============ SupportVectorRegression ===========
R-Squared Value:  0.1378737120259267
Adjusted R-Squared:  0.13743057335310527
MAE:  13.06988492833393
RMSE:  16.373022916538
============================== 

============ DecisionTreeReg ===========
R-Squared Value:  0.14256893256204317
Adjusted R-Squared:  0.14212820726395115
MAE:  13.096891683146907
RMSE:  16.32837753943568
============================== 

============ DeepNeuralNetworkReg ===========
R-Squared Value:  0.14349223949382894
Adjusted R-Squared:  0.14305198878166947
MAE:  13.086412995396548
RMSE:  16.319583733423645
============================== 

============ GradientBoostingReg ===========
R-Squared Value:  0.14293973139502347
Adjusted R-Squared:  0.14249919669000932
MAE:  13.09680566928021
RMSE:  16.32484652785684
============================== 

============ AdaBooostReg ===========
R-Squared Value:  0.1321149414578845
Adjusted R-Squared:  0.13166884273851054
MAE:  13.23053752703005
RMSE:  16.427615585903883
============================== 

============ VotingReg ===========
R-Squared Value:  0.1422489392266164
Adjusted R-Squared:  0.14180804944981185
MAE:  13.115786650644218
RMSE:  16.33142413104539
============================== 

In [54]:
## **4. Regrssion based Algrithms Results with both CV and RFFS**
#with CV and RFFS 
res_rffs_cv = template.MachineLearningwithRFFS_CV_reg(S_eng,"Eng_Score", threshold=5,
                                    algo_list=template.get_supported_algorithms_reg())
cert_degree1_Matriculation     48.119480
cert_degree2_A-Level            8.180574
cert_degree1_Other boards       6.544489
discipline2_Science             6.486983
city_Others                     4.441244
gender_Female                   3.998379
studied_maths_Yes               2.584433
Province_No_Sindh               1.840755
cert_degree1_O-Level            1.755020
cert_degree2_Intermediate       1.669336
city_Karachi-1                  1.431928
Province_Sindh                  1.318947
studied_maths_No                1.285581
city_Karachi-2                  1.212501
gender_Male                     1.155817
city_Hyderabad                  1.126682
city_Islamabad                  1.081441
city_Karachi-3                  1.070356
cert_degree2_Other boards       1.022953
discipline2_Arts                0.832759
cert_degree1_Aga Khan Board     0.779103
city_Lahore                     0.773639
cert_degree2_Aga Khan Board     0.493797
city_Quetta                     0.455373
city_Peshawar                   0.338430
dtype: float64
Selected Features =['cert_degree1_Matriculation', 'cert_degree2_A-Level', 'cert_degree1_Other boards', 'discipline2_Science']
(38931, 26)
============ LinearReg ===========
{'r2': -0.08342737592367298, 'r2_adjusted': -0.0845419840032135, 'mae': 13.568115501842925, 'rmse': 16.52474704353112}
============================== 

============ RidgeReg ===========
{'r2': -0.08342730934561178, 'r2_adjusted': -0.08454191735665532, 'mae': 13.568115315961322, 'rmse': 16.524746619578906}
============================== 

============ LassoReg ===========
{'r2': -0.08339329434585026, 'r2_adjusted': -0.08450786734149843, 'mae': 13.569232263207661, 'rmse': 16.525068463462333}
============================== 

============ RandomForestReg ===========
{'r2': -0.08222382142207117, 'r2_adjusted': -0.08333719133734113, 'mae': 13.56436537390814, 'rmse': 16.519348738346736}
============================== 

============ SupportVectorRegression ===========
{'r2': -0.1025213812593491, 'r2_adjusted': -0.10365563376743532, 'mae': 13.65869924303947, 'rmse': 16.66127814177455}
============================== 

============ DecisionTreeReg ===========
{'r2': -0.08220097708436315, 'r2_adjusted': -0.08331432350590594, 'mae': 13.563859444669683, 'rmse': 16.5187696290696}
============================== 

============ DeepNeuralNetworkReg ===========
{'r2': -0.08221029177649627, 'r2_adjusted': -0.08332364834542844, 'mae': 13.565084542760095, 'rmse': 16.516366330709296}
============================== 

============ GradientBoostingReg ===========
{'r2': -0.0820968837946062, 'r2_adjusted': -0.08321012310469468, 'mae': 13.563312356517397, 'rmse': 16.518145793289637}
============================== 

============ AdaBooostReg ===========
{'r2': -0.07739814170893856, 'r2_adjusted': -0.07850654568770091, 'mae': 13.5673623343632, 'rmse': 16.481546957400717}
============================== 

============ VotingReg ===========
{'r2': -0.07805544918865742, 'r2_adjusted': -0.07916453041533844, 'mae': 13.547408349605249, 'rmse': 16.488253344115112}
============================== 

Appendix-D: ANOVA Analysis of English and Mathematics Scores Comparisons Across all Categorical Variables

In [55]:
#seting data for plots
FILE_NAME = "FinalData_testing.csv"
df3 = template.load_data(FILE_NAME)
df3 = template.cleaningup(df3, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['term_name','date_of_birth', 'place_of_birth', 
                                         'postal_address', 'city1', 'countryname','seat_no',
                                         'medium','test_center','interview_successful', 
                                         'discipline_Mat'])
#df3['Year'].values.astype(str)
#,to_date=['Year'])
df is all cleaned up..
In [56]:
df4=template.cleaningup(df3, to_numeric=[], cols_to_interpolate=[],
                         cols_to_delete=['Year'])
template.ANOVA_analysis(df4)
df is all cleaned up..
============+++++============+++++============
Analysis of Columns Eng_Score by gender
============+++++============+++++============

Anova => - gender
sum_sq df F PR(>F)
Intercept 3.014502e+07 1.0 97391.215552 0.000000e+00
C(Q("gender")) 8.660059e+04 1.0 279.785440 1.384166e-62
Residual 1.204950e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by gender
============+++++============+++++============

Anova => - gender
sum_sq df F PR(>F)
Intercept 1.592565e+07 1.0 55044.080352 0.000000e+00
C(Q("gender")) 1.049090e+05 1.0 362.598437 1.779221e-80
Residual 1.126315e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by Province
============+++++============+++++============

Anova => - Province
sum_sq df F PR(>F)
Intercept 1.455305e+05 1.0 468.910464 2.258974e-103
C(Q("Province")) 5.600229e+04 7.0 25.777669 1.912233e-35
Residual 1.208010e+07 38923.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by Province
============+++++============+++++============

Anova => - Province
sum_sq df F PR(>F)
Intercept 9.095705e+04 1.0 311.841085 1.620726e-69
C(Q("Province")) 1.509261e+04 7.0 7.392022 6.650152e-09
Residual 1.135297e+07 38923.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by city
============+++++============+++++============

Anova => - city
sum_sq df F PR(>F)
Intercept 2.877417e+06 1.0 9450.781736 0.000000e+00
C(Q("city")) 2.857754e+05 8.0 117.327487 6.429085e-195
Residual 1.185032e+07 38922.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by city
============+++++============+++++============

Anova => - city
sum_sq df F PR(>F)
Intercept 1.986099e+06 1.0 6808.878721 0.000000e+00
C(Q("city")) 1.480408e+04 8.0 6.344046 2.966714e-08
Residual 1.135325e+07 38922.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by test_successful
============+++++============+++++============

Anova => - test_successful
sum_sq df F PR(>F)
Intercept 5.780768e+07 1.0 206774.733225 0.0
C(Q("test_successful")) 1.252782e+06 1.0 4481.128247 0.0
Residual 1.088332e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by test_successful
============+++++============+++++============

Anova => - test_successful
sum_sq df F PR(>F)
Intercept 3.465520e+07 1.0 151196.208931 0.0
C(Q("test_successful")) 2.445267e+06 1.0 10668.385871 0.0
Residual 8.922792e+06 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by cert_degree1
============+++++============+++++============

Anova => - cert_degree1
sum_sq df F PR(>F)
Intercept 2.058587e+06 1.0 7410.358798 0.0
C(Q("cert_degree1")) 1.322233e+06 3.0 1586.560912 0.0
Residual 1.081387e+07 38927.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by cert_degree1
============+++++============+++++============

Anova => - cert_degree1
sum_sq df F PR(>F)
Intercept 1.135856e+06 1.0 4553.895797 0.0
C(Q("cert_degree1")) 1.658690e+06 3.0 2216.683801 0.0
Residual 9.709370e+06 38927.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by cert_degree2
============+++++============+++++============

Anova => - cert_degree2
sum_sq df F PR(>F)
Intercept 4.639299e+07 1.0 160595.265526 0.0
C(Q("cert_degree2")) 8.908121e+05 3.0 1027.886759 0.0
Residual 1.124529e+07 38927.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by cert_degree2
============+++++============+++++============

Anova => - cert_degree2
sum_sq df F PR(>F)
Intercept 3.115302e+07 1.0 113883.053281 0.0
C(Q("cert_degree2")) 7.194733e+05 3.0 876.702924 0.0
Residual 1.064859e+07 38927.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by studied_maths
============+++++============+++++============

Anova => - studied_maths
sum_sq df F PR(>F)
Intercept 3.176566e+07 1.0 102828.769799 0.000000e+00
C(Q("studied_maths")) 1.102276e+05 1.0 356.818103 3.143113e-79
Residual 1.202587e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by studied_maths
============+++++============+++++============

Anova => - studied_maths
sum_sq df F PR(>F)
Intercept 1.805425e+07 1.0 61907.726386 0.000000e+00
C(Q("studied_maths")) 1.513008e+04 1.0 51.880802 6.004120e-13
Residual 1.135293e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by discipline2
============+++++============+++++============

Anova => - discipline2
sum_sq df F PR(>F)
Intercept 3.669140e+07 1.0 117762.228397 0.000000
C(Q("discipline2")) 6.917344e+03 1.0 22.201438 0.000002
Residual 1.212918e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by discipline2
============+++++============+++++============

Anova => - discipline2
sum_sq df F PR(>F)
Intercept 2.288393e+07 1.0 78421.409334 0.000000e+00
C(Q("discipline2")) 8.296452e+03 1.0 28.431279 9.762422e-08
Residual 1.135976e+07 38929.0 NaN NaN
============+++++============+++++============
Analysis of Columns Eng_Score by program
============+++++============+++++============

Anova => - program
sum_sq df F PR(>F)
Intercept 3.683319e+07 1.0 120462.186487 0.000000e+00
C(Q("program")) 2.341742e+05 5.0 153.172400 1.122473e-161
Residual 1.190192e+07 38925.0 NaN NaN
============+++++============+++++============
Analysis of Columns Math_Score by program
============+++++============+++++============

Anova => - program
sum_sq df F PR(>F)
Intercept 2.306720e+07 1.0 81525.351325 0.000000e+00
C(Q("program")) 3.544185e+05 5.0 250.520992 2.173036e-264
Residual 1.101364e+07 38925.0 NaN NaN

The results of ANOVA analysis indicate that the English and Mathematics is significantly affected by the categorical columns, which are:

{'gender', 'Province', 'city', 'program','test_successful', 'cert_degree2','discipline2','studied_maths'}

The average English and Mathematics scores significantly vary across the categories in each of the above variables, which is also evident in Appendix-E.

Appendix-E: Average English and Mathematics Scores Comparisons Across all Categorical Variables

In [57]:
template.Avg_by_cat1(df3,cat_list=['Year','gender', 'Province', 'city', 'program','test_successful', 'cert_degree2',
       'discipline2','studied_maths'])
Analysis of Average English and Mathematics Score by Year
Analysis of Average English and Mathematics Score by gender
Analysis of Average English and Mathematics Score by Province
Analysis of Average English and Mathematics Score by city
Analysis of Average English and Mathematics Score by program
Analysis of Average English and Mathematics Score by test_successful
Analysis of Average English and Mathematics Score by cert_degree2
Analysis of Average English and Mathematics Score by discipline2
Analysis of Average English and Mathematics Score by studied_maths

Average English and Mathematics Score Analysis across Categorical Variables

• The average English score is higher for females’ candidates as compared to the male candidates, however, the Mathematics score of male applicants is significantly higher than the female.

• The average English score for backward areas such as Balochistan and KPK (except Gilgit) is lower than the average score of Islamabad, Punjab and Sindh students. The students coming from abroad has the highest English score. For Mathematics, the average score of Balochistan and KPK students is higher or almost similar to the students coming from Sindh, Punjab and AJK. The students from Gilgit are performing very well in both scores as compared to other far flunged areas.

• The student from Karachi 1 (which is DHA and Clifton area of Karachi), Lahore and Islamabad are equally well in English and have the highest score, while in Mathematics are not worse than the other cities.

• The BBA, ACF, SSLA and Economics students has the higher average score in English than the CS and BSEM students, while the latter group outperform the former in Mathematics section of the test.

• The A-level students have significant higher score than the students studying other curriculums both in English and Mathematics. The A-Level and other international boards students have almost same score in English, however the A-level students outperform them in the Mathematics part of the test.

Appendix-F: Percentage Counts Analysis of all Categorical Variables

In [58]:
template.Count_Per1(df3, label_col=['Year', 'gender','program', 'Province','city', 'cert_degree2',
                                    'discipline2', 'test_successful','studied_maths'], 
                   cat_list=['Year', 'gender','program', 'Province','city', 'cert_degree2','discipline2', 
                             'test_successful','studied_maths'])
  
Analsis of Year by gender
Analsis of Year by program
Analsis of Year by Province
Analsis of Year by city
Analsis of Year by cert_degree2
Analsis of Year by discipline2
Analsis of Year by test_successful
Analsis of Year by studied_maths
Analsis of gender by Year
  
Analsis of gender by program
Analsis of gender by Province
Analsis of gender by city
Analsis of gender by cert_degree2
Analsis of gender by discipline2
Analsis of gender by test_successful
Analsis of gender by studied_maths
Analsis of program by Year
Analsis of program by gender
  
Analsis of program by Province
Analsis of program by city
Analsis of program by cert_degree2
Analsis of program by discipline2
Analsis of program by test_successful
Analsis of program by studied_maths
Analsis of Province by Year
Analsis of Province by gender
Analsis of Province by program
  
Analsis of Province by city
Analsis of Province by cert_degree2
Analsis of Province by discipline2
Analsis of Province by test_successful
Analsis of Province by studied_maths
Analsis of city by Year
Analsis of city by gender
Analsis of city by program
Analsis of city by Province
  
Analsis of city by cert_degree2
Analsis of city by discipline2
Analsis of city by test_successful
Analsis of city by studied_maths
Analsis of cert_degree2 by Year
Analsis of cert_degree2 by gender
Analsis of cert_degree2 by program
Analsis of cert_degree2 by Province
Analsis of cert_degree2 by city
  
Analsis of cert_degree2 by discipline2
Analsis of cert_degree2 by test_successful
Analsis of cert_degree2 by studied_maths
Analsis of discipline2 by Year
Analsis of discipline2 by gender
Analsis of discipline2 by program
Analsis of discipline2 by Province
Analsis of discipline2 by city
Analsis of discipline2 by cert_degree2
  
Analsis of discipline2 by test_successful
Analsis of discipline2 by studied_maths
Analsis of test_successful by Year
Analsis of test_successful by gender
Analsis of test_successful by program
Analsis of test_successful by Province
Analsis of test_successful by city
Analsis of test_successful by cert_degree2
Analsis of test_successful by discipline2
  
Analsis of test_successful by studied_maths
Analsis of studied_maths by Year
Analsis of studied_maths by gender
Analsis of studied_maths by program
Analsis of studied_maths by Province
Analsis of studied_maths by city
Analsis of studied_maths by cert_degree2
Analsis of studied_maths by discipline2
Analsis of studied_maths by test_successful
  

Appendix-F

Percentage Counts of Categorical Variables Analysis

The analysis of data across year shows that

• percentage of male and female applicants is consistent over the years. On an average 35.85% of the applicants were female whereas 64.14% were male

• BBA as the most prominent degree as over the years it had most number of applicants. BSECO has the lowest percentage of applicants as the degree program was introduced later.

• most number of applicants were populated by those residing in the Sindh region

• Karachi (the capital city of the province Sindh) was the most popular area from where applications came in.

• there were two almost equal divisions in the applicants. On an average 49.78% of the applicants had A-Level background whereas 46.98% of the applicants did intermediate.

• majority of the applicants had science background.

• reflects that over the years’ success rate has decreased by approximately 8% and by similar percentage the number of unsuccessful candidates have increased.

• It is clearly evident that those applying at the institute had a mathematical background as they studied the subject before joining.

Analysis of Column gender Gender based analysis reveals that

• Percentage of female applicants in the year 2015 almost doubled to what it was in 2014. Over the years the percentage of both male and female applicants have gradually increased, however, both genders witnessed a slight dip in the year 2017.

• the degree program of BBA & SSLA is dominated by female applicants. Comparatively, the degrees such as BSACF & BSCS was tilted towards the male.

• male applicants have taken the lead in all provinces except for the Foreign & Sindh state. The Foreign & Sindh region is the only anomaly where percentage of female applicants is greater than male applicants.

• Sindh’s popular city of Karachi had more female applicants than male applicants. In other provinces it was the opposite as the percentage of male applicants was greater.

• Most percentage of the female applicants had the background of A-Level education system. Whereas the male population was almost equally distributed A-Level and Intermediate Board.

• Majority of the female applicants had Arts background whereas male applicants had Science background.

• There is almost a difference of 1% between pass and fail between the two genders. Female candidates for more unsuccessful vis-à-vis to the male candidates.

• Mathematics was more popular amongst male candidates than it was in female candidates.

Analysis of Column program

Program wise analysis shows that

• BBA and BSACF maintain some form of stability in the number of applicants received each year. BSCS witnessed a dramatic drop in 2017 followed by and exponential rise.

• The percentage of male applicants in all programs have been greater than the percentage of female applicants. However, this is not the case with SSLA as the percentage of female applicants is more than two times that of male candidates.

• BSCS is most popular in Sindh, while Punjab, Islamabad & Balochistan prefer BSECO as their most sorted degree. AJK, Foreign & Gilgit are more tilted towards the BSEM program

• In all degree programs except BSCS, applicants from Karachi were above average as compared to the others across all degree programs. SSLA score the lowest in Peshawar & Quetta as compared to other cities. • A Level & Intermediate was the most popular form of higher education amongst applicants. Most A Level applicants preferred BSEC whereas the Intermediate lot preferred BSCS. SSLA was most popular in the Aga Khan Board and those from the Other boards tilted towards BSEM

• About 69% of the Arts students preferred BSECO whereas 92% of the Science students chose BSCS

• the percentage of unsuccessful applicants across all programs was greater than the percentage of successful ones.

• 99% of those who studied math in their higher education pursued this as their degree by opting for BSEM. Those with non-mathematical background considered BSECO as their preferred degree.

Analysis of Column Province • the year 2019 witnessed the greatest number of applicants from AJK and Gilgit. In addition to this, in the year 2014 Gilgit had one of the lowest number of applicants. • AJK displayed the highest percentage of male applicants and lowest percentage of female applicants. The highest number of female applicants were from Sindh. • The highest percentage of applicants across all program was for BBA applicants from Baluchistan followed by applications for BBA from Sindh. • The city Hyderabad has the lowest turnout from Sindh. • A-Level was the least preferred choice for higher education in Gilgit. On the other hand, intermediate had the highest number of applicants across board from Baluchistan. • Foreign was the only state where Arts was selected, all other provinces clearly focused on Science.

• across all provinces the rate of unsuccessful candidates was far greater than the rate of successful candidates. • more than half of the applicants across board studied math in their higher education.

Analysis of Column city • The percentage of applicants across cities have seen a gradual rise except for a slight dip in 2017 in the number of applicants from all cities. • The percentage of male applicants was the highest from Peshawar whereas most female applicants were from Sindh. • The highest percentage of applicants was witnessed for BBA degree from Quetta whereas the lowest popularity was of BSECO in Hyderabad. • Aga Khan Board was the least popular in Lahore whereas, Intermediate had the highest percentage in Quetta.
• Arts was most popular in Sindh’s capital hub Karachi whereas, Science was most popular amongst Others and Quetta. • As blatantly evident the percentage of successful candidates were far below the percentage of unsuccessful candidates. • More than 50% of the applicants across all cities studied mathematics beforehand.

Analysis of Column cert_degree2

• The Aga Khan Board saw the highest percentage in 2017. Ina addition to this, the number of applicants in Other Boards have gradually increased.

• Percentage of Male candidates was highest from Intermediate, whereas most female candidates were from Aga Khan Board. • BBA was clearly the top scorer amongst claimants from all the different types of higher education certification. • A-Level & Intermediate board had the highest percentage amongst candidates from Sindh. Whereas, A-level in particular was the least preferred mode of higher education in AJK & Gilgit. • The Aga Khan Board scored the highest percentage in Karachi 3 and lowest in Lahore. Similarly, A-Level had the highest percentage in Karachi 3 and lowest in Quetta. • Arts had the highest percentage amongst candidates who did A Level whereas Science was most popular amongst those who did the Intermediate Board. • The percentage of unsuccessful candidates is greater than successful candidates across all higher education degree certification. • 80% of Aga Khan Board candidates studied mathematics Analysis of Column discipline2

• Arts was a popular discipline amongst female candidates whereas, the male candidates preferred Science. • BBA was the most sought after degree for candidates having both Arts & Science background. • Sindh had the highest percentage of candidates from both Arts & Science background. Arts scored the lowest in AJK & Gilgit. • Karachi 3 had the highest percentage of both Arts & Science candidates. Lowest percentages were evident for Arts candidates in Peshawar & Quetta. • Candidates perusing Arts & Science both were most likely to fall in for A-level higher education degree certification.
• Lie evident across other variables, in this case too, the percentage of unsuccessful candidates was far greater than those that were successful. • those who selected the Arts discipline did not study math whereas those who took Science studied math earlier Analysis of Column test_successful • the percentage of unsuccessful candidates was the highest in 2019 and the percentage of successful candidates was the highest in 2018. • 65% of the Male candidates were successful whereas only 34% of the female candidates were successful. • BSCS & BSEM are the only variances whereby the percentage of successful candidate is greater than the percentage of unsuccessful candidates • The highest percentage highlighted was the success percentage in Sindh (88%) whereas the lowest percentage of success rate was from AJK • The highest number of unsuccessful candidates were from Karachi 3 whereas Peshawar had the lowest rate of unsuccessful candidates. • the highest percentage of successful candidates were those with A Level background. On the contrary the highest percentage of unsuccessful candidates were from the Intermediate Board. • those who had Science background managed to have a greater success rate than those with Arts background. • Those who studied math had a greater success rate than those who didn’t.

Analysis of Column studied Mathematics • Over the years those who studied math is decreasing whereas percentage of those candidates who did not have math is increasing. • 69% of the male candidate had taken math in their higher education whereas only 30% of the female candidates had studied math. • Most candidates with or without mathematical background applied for the BA degree program. • The highest percentage of those who did not study math was from Sindh, whereas, the lowest percentage of those who did not study math was from AJK • the highest percentage of those who did not study math were from Karachi. On the other hand, the lowest percentage of those of did not study math were from Quetta. • Most A-Level candidates did not study math, however, most of Intermediate candidates did study math earlier on. • Those from Arts background did not study math but those with Science background had mathematical eloquence. • The percentage of unsuccessful applicants was greater than successful candidates irrespective of non-math versus mathematical background.

In [ ]: